eBay:iPads:: sold classification:: ebayipads_selmdl2_ensCSM
bdanalytics
Date: (Thu) Nov 12, 2015
Introduction:
Data: Source: Training: https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTrain.csv
New: https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTest.csv
Time period:
Synopsis:
Based on analysis utilizing <> techniques,
Regression results: First run:
Classification results: template: prdline.my == “Unknown” -> 296 Low.cor.X.glm: Leaderboard: 0.83458 -> Rank 288 / 1884 0.85514 newobs_tbl=[N=471, Y=327]; submit_filename=template_Final_glm_submit.csv OOB_conf_mtrx=[YN=125, NY=76]=201; max.Accuracy.OOB=0.7710; opt.prob.threshold.OOB=0.6 startprice=100.00; biddable=95.42; productline=49.22; D.T.like=29.75; D.T.use=26.32; D.T.box=21.53;
prdline: -> Worse than template prdline.my == “Unknown” -> 285 All.X.no.rnorm.rf: Leaderboard: 0.82649 newobs_tbl=[N=485, Y=313]; submit_filename=prdline_Final_rf_submit.csv OOB_conf_mtrx=[YN=119, NY=80]=199; max.Accuracy.OOB=0.8339; opt.prob.threshold.OOB=0.5 startprice=100.00; biddable=84.25; D.sum.TfIdf=7.28; D.T.use=4.26; D.T.veri=2.78; D.T.scratch=1.99; D.T.box=; D.T.like=; Low.cor.X.glm: Leaderboard: 0.81234 newobs_tbl=[N=471, Y=327]; submit_filename=prdline_Low_cor_X_glm_submit.csv OOB_conf_mtrx=[YN=125, NY=74]=199; max.Accuracy.OOB=0.8205; opt.prob.threshold.OOB=0.6 startprice=100.00; biddable=96.07; prdline.my=51.37; D.T.like=29.39; D.T.use=25.43; D.T.box=22.27; D.T.veri=; D.T.scratch=;
oobssmpl: -> Low.cor.X.glm: Leaderboard: 0.83402 newobs_tbl=[N=440, Y=358]; submit_filename=oobsmpl_Final_glm_submit OOB_conf_mtrx=[YN=114, NY=84]=198; max.Accuracy.OOB=0.7780; opt.prob.threshold.OOB=0.5 startprice=100.00; biddable=93.87; prdline.my=60.48; D.sum.TfIdf=; D.T.condition=8.69; D.T.screen=7.96; D.T.use=7.50; D.T.veri=; D.T.scratch=;
category: -> Low.cor.X.glm: Leaderboard: 0.82381 newobs_tbl=[N=470, Y=328]; submit_filename=category_Final_glm_submit OOB_conf_mtrx=[YN=119, NY=57]=176; max.Accuracy.OOB=0.8011; opt.prob.threshold.OOB=0.6 startprice=100.00; biddable=79.19; prdline.my=55.22; D.sum.TfIdf=; D.T.ipad=27.05; D.T.like=21.44; D.T.box=20.67; D.T.condition=; D.T.screen=;
dataclns: -> All.X.no.rnorm.rf: Leaderboard: 0.82211 newobs_tbl=[N=485, Y=313]; submit_filename=dataclns_Final_rf_submit OOB_conf_mtrx=[YN=104, NY=75]=179; max.Accuracy.OOB=0.7977; opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=65.85; prdline.my=7.74; D.sum.TfIdf=; D.T.use=2.01; D.T.condition=1.87; D.T.veri=1.62; D.T.ipad=; D.T.like=; Low.cor.X.glm: Leaderboard: 0.79264 newobs_tbl=[N=460, Y=338]; submit_filename=dataclns_Low_cor_X_glm_submit OOB_conf_mtrx=[YN=113, NY=74]=187; max.Accuracy.OOB=0.7977; opt.prob.threshold.OOB=0.5 -> different from prev run of 0.6 biddable=100.00; startprice.log=91.85; prdline.my=38.34; D.sum.TfIdf=; D.T.ipad=29.92; D.T.box=27.76; D.T.work=25.79; D.T.use=; D.T.condition=;
txtterms: -> top_n = c(10) Low.cor.X.glm: Leaderboard: 0.81448 newobs_tbl=[N=442, Y=356]; submit_filename=txtterms_Final_glm_submit OOB_conf_mtrx=[YN=113, NY=69]=182; max.Accuracy.OOB=0.7943; opt.prob.threshold.OOB=0.5 biddable=100.00; startprice.log=90.11; prdline.my=37.65; D.sum.TfIdf=; D.T.ipad=28.67; D.T.work=24.90; D.T.great=21.44; # [1] “D.T.condit” “D.T.condition” “D.T.good” “D.T.ipad” “D.T.new”
# [6] “D.T.scratch” “D.T.screen” “D.T.this” “D.T.use” “D.T.work”
All.X.glm: Leaderboard: 0.81016
newobs_tbl=[N=445, Y=353]; submit_filename=txtterms_Final_glm_submit
OOB_conf_mtrx=[YN=108, NY=72]=180; max.Accuracy.OOB=0.7966;
opt.prob.threshold.OOB=0.5
biddable=100.00; startprice.log=88.24; prdline.my=33.81; D.sum.TfIdf=;
D.T.scratch=25.51; D.T.use=18.97; D.T.good=16.37; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.great” “D.T.excel” “D.T.work” “D.T.ipad”
Max.cor.Y.rpart: Leaderboard: 0.79258
newobs_tbl=[N=439, Y=359]; submit_filename=txtterms_Final_rpart_submit
OOB_conf_mtrx=[YN=105, NY=76]=181; max.Accuracy.OOB=0.7954802;
opt.prob.threshold.OOB=0.5
startprice.log=100; biddable=; prdline.my=; D.sum.TfIdf=;
D.T.scratch=; D.T.use=; D.T.good=; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
All.X.no.rnorm.rf: Leaderboard: 0.80929
newobs_tbl=[N=545, Y=253]; submit_filename=txtterms_Final_rf_submit
OOB_conf_mtrx=[YN=108, NY=61]=169; max.Accuracy.OOB=0.8090395
opt.prob.threshold.OOB=0.5
startprice.log=100.00; biddable=78.82; idseq.my=63.43; prdline.my=45.57;
D.T.use=2.76; D.T.condit=2.35; D.T.scratch=2.00; D.T.good=; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
txtclstr: All.X.no.rnorm.rf: Leaderboard: 0.79363 -> 0.79573 newobs_tbl=[N=537, Y=261]; submit_filename=txtclstr_Final_rf_submit OOB_conf_mtrx=[YN=104, NY=61]=165; max.Accuracy.OOB=0.8135593 opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=79.99; idseq.my=64.94; prdline.my=4.14; prdline.my.clusterid=1.15; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
dupobs: All.X.no.rnorm.rf: Leaderboard: 0.79295 newobs_tbl=[N=541, Y=257]; submit_filename=dupobs_Final_rf_submit OOB_conf_mtrx=[YN=114, NY=65]=179; max.Accuracy.OOB=0.7977401 opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=94.49; idseq.my=67.40; prdline.my=4.48; prdline.my.clusterid=1.99; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
All.X.no.rnorm.rf: Leaderboard: 0.79652
newobs_tbl=[N=523, Y=275]; submit_filename=dupobs_Final_rf_submit
OOB_conf_mtrx=[YN=114, NY=65]=179; max.Accuracy.OOB=0.7977401
opt.prob.threshold.OOB=0.5
startprice.log=100.00; biddable=94.24; idseq.my=67.92;
prdline.my=4.33; prdline.my.clusterid=2.17; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
csmmdl: All.X.no.rnorm.rf: Leaderboard: 0.79396 newobs_tbl=[N=525, Y=273]; submit_filename=csmmdl_Final_rf_submit OOB_conf_mtrx=[YN=111, NY=66]=177; max.Accuracy.OOB=0.8000000 opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=90.30; idseq.my=67.06; prdline.my=4.40; cellular.fctr=3.57; prdline.my.clusterid=2.08;
All.Interact.X.no.rnorm.rf: Leaderboard: 0.77867 newobs_tbl=[N=564, Y=234]; submit_filename=csmmdl_Final_rf_submit OOB_conf_mtrx=[YN=120, NY=53]=173; max.Accuracy.OOB=0.8045198 opt.prob.threshold.OOB=0.5 biddable=100.00; startprice.log=93.99; idseq.my=57.30; prdline.my=9.09; cellular.fctr=3.30; prdline.my.clusterid=2.35;
All.Interact.X.no.rnorm.rf: Leaderboard: 0.77152 newobs_tbl=[N=539, Y=259]; submit_filename=csmmdl_Final_rf_submit OOB_conf_mtrx=[YN=, NY=]=; max.Accuracy.OOB=0.8011299 opt.prob.threshold.OOB=0.5 biddable=100.00; startprice.log=94.93; idseq.my=57.12; prdline.my=9.29; cellular.fctr=3.20; prdline.my.clusterid=2.50; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
All.X.glmnet:
fit_RMSE=???; OOB_RMSE=115.1247; new_RMSE=115.1247;
prdline.my.fctr=100.00; condition.fctrNew=88.53; D.npnct09.log=84.34
biddable=16.48; idseq.my=57.27;spdiff:
All.Interact.X.no.rnorm.rf: Leaderboard: 0.78218 newobs_tbl=[N=517, Y=281]; submit_filename=spdiff_Final_rf_submit OOB_conf_mtrx=[YN=121, NY=38]=159; max.Accuracy.OOB=0.8203390 opt.prob.threshold.OOB=0.6 biddable=100.00; startprice.diff=57.53; idseq.my=41.31; prdline.my=11.43; cellular.fctr=2.36; prdline.my.clusterid=1.82;
All.X.no.rnorm.rf:
fit_RMSE=92.19; OOB_RMSE=130.86; new_RMSE=130.86;
biddable=100.00; prdline.my.fctr=61.92; idseq.my=57.77;
condition.fctr=29.53; storage.fctr=11.22; color.fctr=6.69;
cellular.fctr=6.11
All.X.no.rnorm.rf: Leaderboard: 0.77443
newobs_tbl=[N=606, Y=192]; submit_filename=spdiff_Final_rf_submit
OOB_conf_mtrx=[YN=112, NY=28]=140; max.Accuracy.OOB=0.8418079
opt.prob.threshold.OOB=0.6
startprice.diff=100.00; biddable=96.53; idseq.my=38.10;
prdline.my=3.65; cellular.fctr=2.21; prdline.my.clusterid=0.91; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
color: All.Interact.X.glmnet: fit_RMSE=88.64520; prdline.my.fctr:D.TfIdf.sum.stem.stop.Ratio=100.00; prdline.my.fctr:condition.fctr=77.35 D.TfIdf.sum.stem.stop.Ratio=68.18 prdline.my.fctr:color.fctr=68.12 prdline.my.fctr:storage.fctr=63.32
All.X.no.rnorm.rf: Leaderboard: 0.80638
newobs_tbl=[N=550, Y=248]; submit_filename=color_Final_rf_submit
OOB_conf_mtrx=[YN=108, NY=54]=162; max.Accuracy.OOB=0.8169492
opt.prob.threshold.OOB=0.5
biddable=100.00; startprice.diff=77.90; idseq.my=48.49;
D.ratio.sum.TfIdf.nwrds=6.48; storage.fctr=4.74;
D.TfIdf.sum.stem.stop.Ratio=4.57; prdline.my=4.32;[1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
All.Interact.X.no.rnorm.rf: Leaderboard: 0.72974
newobs_tbl=[N=682, Y=116]; submit_filename=assctxt_Final_rf_submit
OOB_conf_mtrx=[YN=125, NY=43]=168; max.Accuracy.OOB=0.8101695; max.auc.OOB=???;
opt.prob.threshold.OOB=0.6
biddable=100.00; startprice.diff=51.04; idseq.my=29.51;
startprice.diff:biddable=28.70
prdline.my.fctriPadmini:idseq.my=6.89
Highest max.auc.OOB=???; for model:
All.X.no.rnorm.rf: min.RMSE.fit=1.4967021biddable 100.00000000 idseq.my 98.00292371 startprice.unit9 34.31130220 prdl.my.descr.fctr 18.10984741 D.ratio.sum.TfIdf.nwrds 15.23549621 color.fctrUnknown 14.05520993 D.TfIdf.sum.stem.stop.Ratio 13.00884673 D.ratio.nstopwrds.nwrds 10.51165302
All.X.gbm: Leaderboard: 0.75430
newobs_tbl=[N=582, Y=216]; submit_filename=mdlsel_Final_gbm_submit
OOB_conf_mtrx=[YN=58, NY=65]=123;
max.Accuracy.OOB=0.8617978; max.auc.OOB=0.9367161;
opt.prob.threshold.OOB=0.5startprice.diff 100.0000000 100.00000000 biddable 66.6475055 65.40764971 idseq.my 1.8632456 4.55963698
splogdiff: All.X.gbm: Leaderboard: 0.70111 newobs_tbl=[N=553, Y=245]; submit_filename=splogdiff_Final_gbm_submit OOB_conf_mtrx=[YN=35, NY=101]=136; max.Accuracy.OOB=0.8471910; max.auc.OOB=0.9388912; opt.prob.threshold.OOB=0.3 startprice.log.diff 100.0000000 100.0000000 biddable 86.8563123 88.0261866 idseq.my 8.3580281 2.9054298
Forum Ideas: I then focused on feature engineering, each new variable brought its own little improvement so in the end i just kept adding new ones and let the models do their thing. Here are some i used: model (productline:storage:condition), isNew, model2 (product:isNew), 50 common words from descr, descrLength, capsFactor (% of caps in description), number of cheaper items of same model2, number of dearer items of same model2, priceFactor (vs. mean of price for model), priceFactor2 (vs. mean of price for model2), bigID (if ID> 11000 because there seems to be a huge drop in sales after some time), timeline (year of product launch, reasoning is you want to spend less money on older products).
Get the median startprice for each level of productline and condition. Take the difference from startprice as a new variable. I find median works much better than the mean since startprice is not normally distributed. I also created another binary variable on whether this difference is positive or negative.
Square root startprice
scale and center all the variables except sold, including the dummies.
Prediction Accuracy Enhancement Options:
- import.data chunk:
- which obs should be in fit vs. OOB (currently dirty.0 vs .1 is split 50%)
- inspect.data chunk:
- For date variables
- Appropriate factors ?
- Different / More last* features ?
- For date variables
- scrub.data chunk:
- transform.data chunk:
- derive features from multiple features
- manage.missing.data chunk:
- Not fill missing vars
- Fill missing numerics with a different algorithm
- Fill missing chars with data based on clusters
- extract.features chunk:
- Text variables: move to date extraction chunk ???
- Mine acronyms
- Mine places
- Text variables: move to date extraction chunk ???
- Review set_global_options chunk after features are finalized
[](.png)
Potential next steps include:
- Organization:
- Categorize by chunk
- Priority criteria:
- Ease of change
- Impacts report
- Cleans innards
- Bug report
- all chunks:
- at chunk-end rm(!glb_)
- manage.missing.data chunk:
- cleaner way to manage re-splitting of training vs. new entity
- extract.features chunk:
- Add n-grams for glbFeatsText
- “RTextTools”, “tau”, “RWeka”, and “textcat” packages
- Convert user-specified mutate code to config specs
- Add n-grams for glbFeatsText
- fit.models chunk:
- Prediction accuracy scatter graph:
- Add tiles (raw vs. PCA)
- Use shiny for drop-down of “important” features
Use plot.ly for interactive plots ?
- Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
- move model_type parameter to myfit_mdl before indep_vars_vctr (keep all model_* together)
- create a custom model for rpart that has minbucket as a tuning parameter
varImp for randomForest crashes in caret version:6.0.41 -> submit bug report
- Probability handling for multinomials vs. desired binomial outcome
- ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
extensions toward multiclass classification are scheduled for the next release
- Skip trControl.method=“cv” for dummy classifier ?
- Add custom model to caret for a dummy (baseline) classifier (binomial & multinomial) that generates proba/outcomes which mimics the freq distribution of glb_rsp_var values; Right now glb_dmy_glm_mdl always generates most frequent outcome in training data
glm_dmy_mdl should use the same method as glm_sel_mdl until custom dummy classifer is implemented
- fit.all.training chunk:
- myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
- Compare glb_sel_mdl vs. glb_fin_mdl:
- varImp
- Prediction differences (shd be minimal ?)
- Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
- Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
- Parameterize glb_analytics_pn
- Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
- Replicate myfit_mdl_classification features in myfit_mdl_regression
- Do non-glm methods handle interaction terms ?
- f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
- Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
- Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
- Incorporate code chunks in print_sessionInfo
- Test against
- projects in github.com/bdanalytics
- lectures in jhu-datascience track
Analysis:
rm(list = ls())
set.seed(12345)
options(stringsAsFactors = FALSE)
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mydsutils.R")## Loading required package: caret
## Loading required package: lattice
## Loading required package: ggplot2source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
source("~/Dropbox/datascience/R/mytm.R")
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
registerDoMC(6) # # of cores on machine - 2
suppressPackageStartupMessages(require(caret))
source("~/Documents/Work/PullRequests/caret/pkg/caret/R/confusionMatrix.R")
source("~/Documents/Work/PullRequests/caret/pkg/caret/R/ggplot.R")
#packageVersion("tm")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")
# Analysis control global variables
# Inputs
glb_trnng_url <- "https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTrain.csv"
glb_newdt_url <- "https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTest.csv"
glbInpMerge <- # NULL #: default
list(fnames = c("ebayipads_finmdl_bid0_sp_out.csv", "ebayipads_mdlens_bid1_sp_out.csv"))
glb_is_separate_newobs_dataset <- TRUE # or TRUE
glb_split_entity_newobs_datasets <- FALSE # select from c(FALSE, TRUE)
glb_split_newdata_method <- NULL # select from c(NULL, "condition", "sample", "copy")
glb_split_newdata_condition <- NULL # or "is.na(<var>)"; "<var> <condition_operator> <value>"
glb_split_newdata_size_ratio <- 0.3 # > 0 & < 1
glb_split_sample.seed <- 123 # or any integer
glbObsDropCondition <- # default : NULL
"(UniqueID %in% c(NULL
, 11234 #sold=0; 2 other dups(10306, 11503) are sold=1
, 11844 #sold=0; 3 other dups(11721, 11738, 11812) are sold=1
)) |
(productline %in% c('iPad 5', 'iPad mini Retina'))
# | (biddable != 0) # bid0_sp
# | (biddable == 0) # bid1_sp
"
#parse(text=glbObsDropCondition)
#subset(glb_allobs_df, .grpid %in% c(31))
glb_obs_repartition_train_condition <- NULL
# "!is.na(sold) & (sold == 1)" # : bid._sp
glb_max_fitobs <- NULL # or any integer
glb_is_regression <- FALSE; glb_is_classification <- !glb_is_regression;
glb_is_binomial <- TRUE #or FALSE
glb_rsp_var_raw <- "sold" #: !_sp # "startprice" # : bid._sp #
# for classification, the response variable has to be a factor
glb_rsp_var <- "sold.fctr" #:!_sp # "startprice.log10" :bid._sp # glb_rsp_var_raw :default
# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"),
# or contains spaces (e.g. "Not in Labor Force")
# caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- function(raw) { # NULL
# return(raw ^ 0.5)
# return(log(1 + raw))
# return(log10(raw)) # bid._sp
# return(exp(-raw / 2))
ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == 1, "Y", "N"); return(relevel(as.factor(ret_vals), ref="N"))
# #as.factor(paste0("B", raw))
# #as.factor(gsub(" ", "\\.", raw))
}
# glb_map_rsp_raw_to_var(tst <- c(NA, 0, 1)) # !_sp
# glb_map_rsp_raw_to_var(tst <- c(NA, 0, 2.99, 280.50, 1000.00)) # bid._sp
glb_map_rsp_var_to_raw <- function(var) { # NULL #
# return(var ^ 2.0)
# return(exp(var) - 1)
# return(10 ^ var) # bid._sp
# return(-log(var) * 2)
as.numeric(var) - 1
# #as.numeric(var)
# #gsub("\\.", " ", levels(var)[as.numeric(var)])
# c("<=50K", " >50K")[as.numeric(var)]
# #c(FALSE, TRUE)[as.numeric(var)]
}
# glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(tst))
if ((glb_rsp_var != glb_rsp_var_raw) && is.null(glb_map_rsp_raw_to_var))
stop("glb_map_rsp_raw_to_var function expected")
glb_rsp_var_out <- paste0(glb_rsp_var, ".predict.") # mdl_id is appended later
# List info gathered for various columns
# <col_name>: <description>; <notes>
# description = The text description of the product provided by the seller.
# biddable = Whether this is an auction (biddable=1) or a sale with a fixed price (biddable=0).
# startprice = The start price (in US Dollars) for the auction (if biddable=1) or the sale price (if biddable=0).
# condition = The condition of the product (new, used, etc.)
# cellular = Whether the iPad has cellular connectivity (cellular=1) or not (cellular=0).
# carrier = The cellular carrier for which the iPad is equipped (if cellular=1); listed as "None" if cellular=0.
# color = The color of the iPad.
# storage = The iPad's storage capacity (in gigabytes).
# productline = The name of the product being sold.
# If multiple vars are parts of id, consider concatenating them to create one id var
# If glb_id_var == NULL, ".rownames <- row.names()" is the default
# Derive a numeric feature from id var
# User-specified exclusions
# List feats that shd be excluded due to known causation by prediction variable
glbFeatsExclude <- c(NULL
### !_sp
, "description", "descr.my", "productline"
, "startprice", "startprice.log10.predict", "sprice.predict.diff"
### bid0_sp
# , "description", "productline"
# , "sold", "startprice.log10.cut.fctr"
# # List feats that are linear combinations (alias in glm)
# , "D.terms.post.stem.n.log", "D.weight.sum"
# #, "prdl.descr.my.fctriPad4#1:.clusterid.fctr3" This does not work
# # if RFE is rated lower than Low.cor, list feats that are in RFE & not in Low.cor
# # min.RMSE.fit(RFE.X.glmnet)=0.1138888
# # D.chrs.n.log 61.12483
# # D.chrs.uppr.n.log 61.12483
# # D.ratio.wrds.stop.n.wrds.n 61.12483
# # D.terms.post.stop.n.log 61.12483
# # D.weight.post.stem.sum 61.12483
# # D.wrds.n.log 61.12483
# # D.wrds.stop.n.log 61.12483
# # D.wrds.unq.n.log 61.12483
# #, "startprice.dcm2.is9" # min.RMSE.fit(RFE.X.glmnet)=0.1141991 (up)
# , "D.wrds.stop.n.log" # min.RMSE.fit(RFE.X.glmnet)=0.1131232
### bid0_sp
### bid1_sp
# , "description", "productline"
# , "sold", "startprice.log10.cut.fctr"
### bid1_sp
)
glb_id_var <- c("UniqueID")
glb_category_var <- "prdl.my.fctr" # "prdl.descr.my.fctr" # "productline" # NULL
glb_drop_vars <- c(NULL) # or c("<col_name>")
glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"
glb_assign_pairs_lst <- NULL;
# glb_assign_pairs_lst[["<var1>"]] <- list(from=c(NA),
# to=c("NA.my"))
glb_assign_vars <- names(glb_assign_pairs_lst)
# Derived features
glbFeatsDerive <- NULL;
# Add logs of numerics that are not distributed normally -> do automatically ???
# Right skew: logp1; sqrt; ^ 1/3; logp1(logp1); log10; exp(-<feat>/constant)
# glbFeatsDerive[["prdline.my"]] <- list(
# mapfn=function(productline) { return(productline) }
# , args=c("productline"))
### bid._sp
# glbFeatsDerive[["startprice.log10.cut.fctr"]] <- list(
# mapfn=function(startprice.log10) { return(cut(startprice.log10, 3)) }
# , args=c("startprice.log10"))
### bid._sp
glbFeatsDerive[["sprice.root2"]] <- list(
mapfn = function(startprice) { return(startprice ^ (1/2)) }
, args = c("startprice"))
glbFeatsDerive[["sprice.log10"]] <- list(
mapfn = function(startprice) { return(log(startprice)) }
, args = c("startprice"))
glbFeatsDerive[["sprice.d20nexp"]] <- list(
mapfn = function(startprice) { return(exp(-startprice / 20)) }
, args = c("startprice"))
glbFeatsDerive[["sprice.predict.diff"]] <- list(
mapfn = function(startprice.log10.predict, startprice) {
spdiff <- (10 ^ startprice.log10.predict) - startprice;
return(spdiff) }
, args = c("startprice.log10.predict", "startprice"))
# glbFeatsDerive[["spdiff.root10"]] <- list(
# mapfn = function(sprice.predict.diff) {
# return(sign(sprice.predict.diff) * (abs(sprice.predict.diff) ^ (1/10))) }
# , args = c("sprice.predict.diff"))
glbFeatsDerive[["spdiff.cut.fctr"]] <- list(
mapfn = function(sprice.predict.diff) {
return(cut(sprice.predict.diff, c(-1000, -100, -10, -1, 0, 1, 10, 100, 1000))) }
, args = c("sprice.predict.diff"))
#glb_allobs_df[which(glb_post_stop_words_terms_mtrx_lst[[txt_var]][, subset(glb_post_stop_words_terms_df_lst[[txt_var]], term %in% c("conditionminimal"))$pos] > 0), "description"]
glbFeatsDerive[["descr.my"]] <- list(
mapfn = function(description) { mod_raw <- description;
### bid._sp
# # This is here because it does not work with txt_map_filename
mod_raw <- gsub(paste0(c("\n", "\211", "\235", "\317", "\333"), collapse = "|"), " ",
mod_raw)
# # This should go into txt_map_filename
# mod_raw <- gsub("\\.\\.", "\\. ", mod_raw);
# # Don't parse for "." because of ".com"; use customized gsub for that text
# mod_raw <- gsub("(\\w)(!|\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
#mod_raw <- grep(""", glb_allobs_df$descr.my, value = TRUE)
mod_raw <- gsub("&", "&", mod_raw);
mod_raw <- gsub("<", "<", mod_raw);
mod_raw <- gsub(">", ">", mod_raw);
mod_raw <- gsub("<br>", " ", mod_raw); # line break - add a count for it ???
mod_raw <- gsub(""", " ", mod_raw); # justification meta-character
mod_raw <- gsub("&#(0*)37;", "%", mod_raw);
mod_raw <- gsub("'", "'", mod_raw);
mod_raw <- gsub("([[:digit:]])\\.([[:digit:]])\\.([[:digit:]])",
"\\1point\\2\\point\\3", mod_raw);
mod_raw <- gsub("([[:digit:]])\\.([[:digit:]])", "\\1point\\2", mod_raw);
mod_raw <- gsub("([[:digit:]]),([[:digit:]])", "\\1\\2", mod_raw);
mod_raw <- gsub("\\b1st\\b", "first", mod_raw);
mod_raw <- gsub("\\b2nd\\b", "second", mod_raw);
mod_raw <- gsub("\\b3rd\\b", "third", mod_raw);
mod_raw <- gsub("\\b4th\\b", "fourth", mod_raw);
mod_raw <- gsub("\\.(com|COM)\\b", "dot\\1", mod_raw);
#
# # Modifications for this exercise only
# # Add dictionary to stemDocument e.g. stickers stemmed to sticker ???
# mod_raw <- gsub("8\\.25", "825", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\b10\\.SCREEN\\b", "10\\. SCREEN", mod_raw);
mod_raw <- gsub("\\b128 gb\\b", "128gb", mod_raw);
mod_raw <- gsub("\\b16G\\b", "16GB", mod_raw);
# mod_raw <- gsub(" 16 gig ", " 16gb ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" 16 gb ", " 16gb ", mod_raw, ignore.case=TRUE);
#
# mod_raw <- gsub("\\bAccounts\\b", "Account", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub("\\bactivated\\b", "activate", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub(" actuuly ", " actual ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\badaptor\\b", "adapter", mod_raw);
# mod_raw <- gsub("\\baffects\\b", "affect", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\bair-like\\b", "air -like", mod_raw);
mod_raw <- gsub("\\bALL-JUST\\b", "ALL -JUST", mod_raw);
mod_raw <- gsub("\\bApple's\\b", "Apple'", mod_raw);
# #mod_raw <- glb_allobs_df[c(1322), txt_var]; mod_raw
mod_raw <- gsub("\\bApple care\\b", "Applecare", mod_raw);
mod_raw <- gsub("\\bAT&T\\b", "ATT", mod_raw);
# mod_raw <- gsub(" bacK!wiped ", " bacK ! wiped ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" backplate", " back plate", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\bbarley", "barely", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" bend ", " bent ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\b(B|b)(EST|est) (B|b)(UY|uy)\\b", "\\1\\2\\3\\4", mod_raw);
# mod_raw <- gsub(" black\\.Device ", " black \\. Device ", mod_raw,
# ignore.case=TRUE);
# mod_raw <- gsub("black\\),charger ", "black\\), charger ", mod_raw,
# ignore.case=TRUE);
# mod_raw <- gsub("\\bblacked\\b", "black", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub("\\bblemish\\b", "blemishes", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub(" blocks", " blocked", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" book ", " manual ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\b(B|b)(RAND|rand)( |-)(N|n)(EW|ew)\\b", "\\1\\2\\4\\5", mod_raw)
#mod_raw <- c("brand new", "BRAND new", "brand NEW", "BRAND NEW", "bbrand new", "brand-new", "brand newb")
mod_raw <- gsub("\\bbrokenCharger\\b", "broken Charger", mod_raw);
#
mod_raw <- gsub("\\bC-Major\\b", "C -Major", mod_raw)
# mod_raw <- gsub(" perfectlycord ", " perfectly cord ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\bcord", "cable", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bcables\\.No\\b", "cables. No", mod_raw);
# mod_raw <- gsub("\\bcables\\b", "cable", mod_raw, ignore.case=TRUE);
#
mod_raw <- gsub("\\bcare\\.The\\b", "care\\. The", mod_raw);
# mod_raw <- gsub("\\b(cared|careful|CAREFUL)\\b", "care", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub("\\b(cases|casing)\\b", "case", mod_raw, ignore.case=TRUE);
# #mod_raw <- glb_allobs_df[c(88,187,280,1040,1098), txt_var]; mod_raw
mod_raw <- gsub("\\bCase/Cover\\b", "Case/ Cover", mod_raw);
mod_raw <- gsub("\\bCasing/Screen\\b", "Casing/ Screen", mod_raw);
# mod_raw <- gsub(" carefully ", " careful ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\bchargers\\b", "charger", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\bchip/crack\\b", "chip/ crack", mod_raw);
# mod_raw <- gsub("\\bchips\\b", "chip", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\bCLEAN\\!LIKE\\b", "CLEAN! LIKE", mod_raw);
# mod_raw <- gsub("\\bcleanly\\b", "clean", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub("\\b(C|c)olor(.*)s\\b", "\\1olor", mod_raw, ignore.case=FALSE);
# #mod_raw <- glb_allobs_df[c(280,1411), txt_var]; mod_raw
mod_raw <- gsub("\\bColors,models\\b", "Colors ,models", mod_raw);
# mod_raw <- gsub("\\bcompletely\\b", "complete", mod_raw, ignore.case=FALSE);
# #mod_raw <- glb_allobs_df[c(178), txt_var]; mod_raw
#
mod_raw <- gsub("\\bCONDITION..CLEAN\\b", "CONDITION ..CLEAN", mod_raw);
mod_raw <- gsub("\\bcondition,comes\\b", "condition ,comes", mod_raw);
mod_raw <- gsub("\\bcondition\\.Device\\b", "condition .Device", mod_raw);
mod_raw <- gsub("\\bconditionHas\\b", "condition Has", mod_raw);
mod_raw <- gsub("\\bcondition\\.\\.\\.like\\b", "condition ...like", mod_raw);
mod_raw <- gsub("\\bcondition\\*Minimal\\b", "condition *Minimal", mod_raw);
mod_raw <- gsub("\\bCondition-Moderate\\b", "Condition -Moderate", mod_raw);
mod_raw <- gsub("\\bcondition\\.The\\b", "condition .The", mod_raw);
mod_raw <- gsub("\\bCONDITION\\.VERY\\b", "CONDITION .VERY", mod_raw);
# mod_raw <- gsub(" (conditon|condtion|contidion|conditions)", " condition", mod_raw,
# mod_raw <- gsub("\\b(conditon|condtion|contidion|conditions)\\b", "condition", mod_raw,
# ", "\\1\\. \\2", mod_raw,
# ignore.case=TRUE);
# mod_raw <- gsub("(condition)(Has)", "\\1\\. \\2", mod_raw);
#
# mod_raw <- gsub("\\bCONNECTED\\b", "CONNECT", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub("\\bconnects\\b", "connect", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub(" consist ", " consistent ", mod_raw, ignore.case=TRUE);
# #mod_raw <- glb_allobs_df[c(195, 379, 437), txt_var]; mod_raw
# mod_raw <- gsub("\\bCosmetics\\b", "Cosmetic", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\bCracked/Damaged\\b", "Cracked/ Damaged", mod_raw);
mod_raw <- gsub("\\bcracksNo\\b", "cracks No", mod_raw);
#
# mod_raw <- gsub("\\b(D|d)amaged\\b", "\\1amage", mod_raw, ignore.case=TRUE);
# #mod_raw <- glb_allobs_df[c(116, 1360), txt_var]; mod_raw
# mod_raw <- gsub("\\bDays\\b", "Day", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" DEFAULTING ", " DEFAULT ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\bdefect(ive)*\\b", "defects", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub(" definitely ", " definite ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\b(D|d)ented\\b", "\\1ent", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub(" described", " describe", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" desciption", " description", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" devices", " device", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" Digi\\.", " Digitizer\\.", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\b(ding|dinged)\\b", "dings", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" display\\.New ", " display\\. New ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" displays", " display", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bdo( +)not\\b", "dont", mod_raw);
mod_raw <- gsub("\\b(D|d)oes( +)(N|n)(O|o)(T|t)\\b", "\\1oes\\3\\5", mod_raw);
# mod_raw <- gsub("\\b(drop|drops)\\b", "dropped", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\b(E|e)dge\\b", "\\1dges", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub(" effect ", " affect ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" Excellant ", " Excellent ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" excellently", " excellent", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" EUC ", " excellent used condition", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" feels ", " feel ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bfineiCloud\\b", "fine iCloud", mod_raw, ignore.case = FALSE);
# mod_raw <- gsub(" fine.Its ", " fine. Its ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\bfix\\b", "fixed", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\bflaws\\b", "flaw", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\bflawlessly\\b", "flawless", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" Framing ", " Frame ", mod_raw, ignore.case=TRUE);
#
# mod_raw <- gsub(" functioanlity", " functionality", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\bfunction(ing|ality)\\b", "functional", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" functional\\.Very little ", " functional\\. Very little ", mod_raw,
# ignore.case=TRUE);
mod_raw <- gsub("\\b([[:digit:]]+) (GB|gb)\\b", "\\1\\2", mod_raw);
mod_raw <- gsub("\\b([[:digit:]]+) gig\\b", "\\1gb", mod_raw);
mod_raw <- gsub("\\b(G|g)(EEK|eek) (S|s)(QUAD|quad)\\b", "\\1\\2\\3\\4", mod_raw);
# mod_raw <- gsub("^Gentle ", "Gently ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\(gray color", "\\(spacegray color", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" GREAT\\.SCreen ", " GREAT\\. SCreen ", mod_raw,
# ignore.case=TRUE);
mod_raw <- gsub("\\bGUARANTEES-IT\\b", "GUARANTEES -IT", mod_raw);
# mod_raw <- gsub("\\b(guarantee|guarantees)\\b", "guaranteed", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\ba handful of times\\b", "sparingly", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\bhardly any\\b", "no", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\bhardly ever used\\b", "sparingly used", mod_raw, ignore.case=TRUE);
#
mod_raw <- gsub("\\biCL0UD\\b", "iCLOUD", mod_raw);
mod_raw <- gsub("\\bI (CLOUD|cloud)\\b", "I\\1", mod_raw);
# mod_raw <- gsub("^iPad Black 3rd generation ", "iPad 3 Black ", mod_raw,
# ignore.case=TRUE);
mod_raw <- gsub("\\bIMEINo\\b", "IMEI No", mod_raw);
mod_raw <- gsub("\\bIMIE\\b", "IMEI", mod_raw);
# mod_raw <- gsub("\\bincluding\\b", "included", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub(" install\\. ", " installed\\. ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("inivisible", "invisible", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bI pad\\b", "Ipad", mod_raw);
mod_raw <- gsub("\\b(I|i)(P|p)(A|a)(D|d) (A|a)(I|i)(R|r)\\b", "\\1\\2\\3\\4\\5\\6\\7",
mod_raw);
mod_raw <- gsub("\\b(I|i)(P|p)(A|a)(D|d) (M|m)ini\\b", "\\1\\2\\3\\4\\5ini", mod_raw);
mod_raw <- gsub("\\b(I|i)(P|p)(A|a)(D|d) (M|m)inis\\b", "\\1\\2\\3\\4\\5ini", mod_raw);
mod_raw <- gsub("\\b(IPAD|Ipad|iPad|ipad) ([[:digit:]])\\b", "\\1\\2", mod_raw);
mod_raw <- gsub("\\b(Ipadair|iPadAir|ipadair) ([[:digit:]])\\b", "\\1\\2",
mod_raw);
mod_raw <- gsub("\\b(iPadMini|iPadmini) ([[:digit:]])\\b", "\\1\\2", mod_raw);
mod_raw <- gsub("\\bI Phone\\b", "IPhone", mod_raw);
mod_raw <- gsub("\\bIS-NO\\b", "IS -NO", mod_raw, ignore.case = FALSE)
#
# mod_raw <- gsub(" Keeped ", " Kept ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" knicks ", " nicks ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" lightening ", " lightning ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bLightning-to-USB\\b", "Lightning- to- USB", mod_raw);
mod_raw <- gsub("\\b(L|l)(IKE|ike)( |-)(N|n)(EW|ew)\\b", "\\1\\2\\4\\5", mod_raw);
#mod_raw <- c("like new", "LIKE new", "like NEW", "LIKE NEW", "blike new", "like-new", "like newb")
mod_raw <- gsub("\\bLIKENEW!ONE\\b", "LIKENEW! ONE", mod_raw);
# mod_raw <- gsub("\\b(lock|locks)\\b", "locked", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\blots\\b", "lot", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" manuals ", " manual ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" mars ", " marks ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" marks\\.Absolutely ", " marks\\. Absolutely ", mod_raw,
# ignore.case=TRUE);
# mod_raw <- gsub("\\bmarkings\\b", "marks", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\bminis\\b", "mini", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub(" minimum", " minimal", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" MINT\\.wiped ", " MINT\\. wiped ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\bmonth\\b", "months", mod_raw, ignore.case=TRUE);
# #mod_raw <- glb_allobs_df[c(1803), txt_var]; mod_raw
mod_raw <- gsub("\\bNew-Other\\b", "New -Other", mod_raw);
# mod_raw <- gsub(" NEW\\!(SCREEN|ONE) ", " NEW\\! \\1 ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" new looking$", " looks new", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" newer ", " new ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\bnoted\\b", "note", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" oped ", " opened ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" opening", " opened", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" operated", " operational", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\botter box\\b", "otterbox", mod_raw);
#
# mod_raw <- gsub("\\bpackage\\b", "packaging", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub("\\bPACKAGE\\b", "PACKAGing", mod_raw, ignore.case=FALSE);
# #mod_raw <- glb_allobs_df[c(360, 1142), txt_var]; mod_raw
mod_raw <- gsub("\\bperfectlycord\\b", "perfectly cord", mod_raw);
# mod_raw <- gsub(" performance", " performs", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" personalized ", " personal ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\bPhysically\\b", "Physical", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub("\\b(picture|pictured)\\b", "pictures", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub("\\bPICTURE\\b", "PICTUREs", mod_raw, ignore.case=FALSE);
# #mod_raw <- glb_allobs_df[c(184, 892), txt_var]; mod_raw
# mod_raw <- gsub("\\b[P|p]ower(ed|ing|s)\\b", "\\1ower", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub(" pre- owned ", " used ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\bprevious\\b", "previously", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\bpreviously (owned|used)\\b", "used", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\b(P|p)reviously(.*)(O|o)wned\\b", "\\1reviously\\3wned\\2",
mod_raw);
mod_raw <- gsub("\\b(P|p)reviously(.*)(U|u)sed\\b", "\\1reviously\\3sed\\2", mod_raw);
# mod_raw <- gsub("\\bproblem\\b", "problems", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" products ", " product ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\bprotected\\b", "protector", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub("\\bprotection\\b", "protector", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub("\\bPROTECTION\\b", "PROTECTOR", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\bREADiPad\\b", "READ iPad", mod_raw);
# mod_raw <- gsub(" re- assemble ", " reassemble ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bREFURB\\.", "REFURBished.", mod_raw);
# mod_raw <- gsub(" reponding", " respond", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bright-hand\\b", "right -hand", mod_raw);
# mod_raw <- gsub(" rotation ", " rotate ", mod_raw, ignore.case=TRUE);
#
# mod_raw <- gsub(" Sales ", " Sale ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bScratch-Free\\b", "Scratch- Free", mod_raw);
mod_raw <- gsub("\\bSCRATCHES/BLEMISHES...SCRATCHES\\b", "SCRATCHES/ BLEMISHES... SCRATCHES", mod_raw);
mod_raw <- gsub("\\bscratches,clear\\b", "scratches, clear", mod_raw);
mod_raw <- gsub("\\bScratches/Dent\\b", "Scratches/ Dent", mod_raw);
mod_raw <- gsub("\\bScratches/scuffs/nicks/scrapes\\b", "Scratches/ scuffs/ nicks/ scrapes", mod_raw);
# mod_raw <- gsub(" scratchs ", " scratches ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\b(scratchs|scratching)\\b", "scratches", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\bset up\\b", "setup", mod_raw);
# mod_raw <- gsub(" shipped| Shipment", " ship", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\bshowing\\b", "shows", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\b(shrink|SHRINK) (wrap|WRAP)", "\\1\\2", mod_raw);
# mod_raw <- gsub("\\bshuts\\b", "shut", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" sides ", " side ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" skinned,", " skin,", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\bSlightly\\b", "slight", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\b(Space|space) (G|g)r(a|e)y\\b", "\\1\\2ray", mod_raw);
# mod_raw <- gsub(" spec ", " speck ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bsomescratches\\b", "some scratches", mod_raw);
# mod_raw <- gsub(" Sticker ", " Stickers ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\bstoring", "store", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub("SWAPPA\\.COM", "SWAPPAsdotCOM", mod_raw, ignore.case=TRUE);
#
# mod_raw <- gsub(" T- Mobile", " TMobile", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\b(tear|TEAR)(s|S)\\b", "\\1", mod_raw, ignore.case=FALSE);
# #mod_raw <- glb_allobs_df[c(376), txt_var]; mod_raw
mod_raw <- gsub("\\b(touch|Touch|TOUCH) (screen|SCREEN)\\b", "\\1\\2", mod_raw);
# mod_raw <- gsub("\\bTURN\\b", "TURNS", mod_raw, ignore.case=FALSE);
#
mod_raw <- gsub("\\bUnlockedCracked\\b", "Unlocked Cracked", mod_raw);
# mod_raw <- gsub("\\bUNUSABLE\\b", "UNUSED", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub("\\b(update|updates)\\b", "updated", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub("\\bupgrade\\b", "upgraded", mod_raw, ignore.case=FALSE);
# mod_raw <- gsub(" uppser ", " upper ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("use*Case\\b", "use *Case", mod_raw, ignore.case = FALSE)
# mod_raw <- gsub(" use\\.Scratches ", " use\\. Scratches ", mod_raw,
# ignore.case=TRUE);
#
# mod_raw <- gsub(" verify ", " verified ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" wear\\.Device ", " wear\\. Device ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub("\\bwears\\b", "\\wear", mod_raw, ignore.case=TRUE);
# #mod_raw <- glb_allobs_df[c(167, 272), txt_var]; mod_raw
# mod_raw <- gsub(" whats ", " what's ", mod_raw, ignore.case=TRUE);
# mod_raw <- gsub(" WiFi\\+4G ", " WiFi \\+ 4G ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\b(W|w)(IFI|ifi)( |-)(ONLY|only)\\b", "\\1\\2\\4", mod_raw);
mod_raw <- gsub("\\bwill( +)not\\b", "wont", mod_raw);
mod_raw <- gsub("\\byr\\b", "year", mod_raw);
mod_raw <- gsub("\\bZa(a|g)g Invisible(.*)Shield\\b", "ZaagInvisibleShield", mod_raw);
### bid._sp
return(mod_raw) }
, args = c("description"))
# To identify glb_id_vars with >=10 obs
#mod_raw <- glb_allobs_df[sel_obs(list(descr.my.contains="\\bdoes( +)not\\b")), glbFeatsText]
#mod_raw <- glb_allobs_df[sel_obs(list(descr.my.contains="\\bipad [[:digit:]]\\b")), glbFeatsText][01:10]
#mod_raw <- glb_allobs_df[sel_obs(list(descr.my.contains="pad mini")), glbFeatsText][11:20]
#mod_raw <- glb_allobs_df[sel_obs(list(descr.my.contains="pad mini")), glbFeatsText][21:30]
#mod_raw <- glb_allobs_df[sel_obs(list(descr.my.contains="pad mini")), glbFeatsText][31:40]
glbFeatsDerive[["prdl.my.fctr"]] <- list(
mapfn = function(productline, description) {
as.factor(gsub(" ", "", productline)) }
, args = c("productline"))
# glbFeatsDerive[["prdl.descr.my.fctr"]] <- list(
# mapfn = function(productline, description) {
# as.factor(paste(gsub(" ", "", productline),
# as.numeric(nchar(description) > 0),
# sep = "#")) }
# , args = c("productline", "description"))
#print(mycreate_sqlxtab_df(glb_allobs_df, c("prdl.descr.my.fctr", "sold")))
# mapfn=function(startprice) { return(scale(log(startprice))) }
# , args=c("startprice"))
# mapfn=function(Rasmussen) { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) }
# mapfn=function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
# mapfn=function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
# mapfn=function(Week) { return(substr(Week, 1, 10)) }
# mapfn=function(raw) { tfr_raw <- as.character(cut(raw, 5));
# tfr_raw[is.na(tfr_raw)] <- "NA.my";
# return(as.factor(tfr_raw)) }
# , args=c("raw"))
# mapfn=function(PTS, oppPTS) { return(PTS - oppPTS) }
# , args=c("PTS", "oppPTS"))
# # If glb_allobs_df is not sorted in the desired manner
# mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glb_allobs_df)$ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }
# glbFeatsDerive[["<txt_var>.niso8859.log"]] <- list(
# mapfn=function(<txt_var>) { match_lst <- gregexpr("&#[[:digit:]]{3};", <txt_var>)
# match_num_vctr <- unlist(lapply(match_lst,
# function(elem) length(elem)))
# return(log(1 + match_num_vctr)) }
# , args=c("<txt_var>"))
# mapfn=function(raw) { mod_raw <- raw;
# mod_raw <- gsub("&#[[:digit:]]{3};", " ", mod_raw);
# # Modifications for this exercise only
# mod_raw <- gsub("\\bgoodIn ", "good In", mod_raw);
# return(mod_raw)
# # Create user-specified pattern vectors
# #sum(mycount_pattern_occ("Metropolitan Diary:", glb_allobs_df$Abstract) > 0)
# if (txt_var %in% c("Snippet", "Abstract")) {
# txt_X_df[, paste0(txt_var_pfx, ".P.metropolitan.diary.colon")] <-
# as.integer(0 + mycount_pattern_occ("Metropolitan Diary:",
# glb_allobs_df[, txt_var]))
#summary(glb_allobs_df[ ,grep("P.on.this.day", names(glb_allobs_df), value=TRUE)])
# glb_allobs_df$<descriptor>.my <-
# plyr::revalue(glb_allobs_df$<descriptor>.my, c(
# "ABANDONED BUILDING" = "OTHER",
# "##" = "##"
# ))
# print(<descriptor>_freq_df <- mycreate_sqlxtab_df(glb_allobs_df, c("<descriptor>.my")))
# # print(dplyr::filter(<descriptor>_freq_df, grepl("(MEDICAL|DENTAL|OFFICE)", <descriptor>.my)))
# # print(dplyr::filter(dplyr::select(glb_allobs_df, -<var.zoo>),
# # grepl("STORE", <descriptor>.my)))
# glbFeatsDerive[["<var1>"]] <- glbFeatsDerive[["<var2>"]]
glb_derive_vars <- names(glbFeatsDerive)
# tst <- "descr.my"; args_lst <- NULL; for (arg in glbFeatsDerive[[tst]]$args) args_lst[[arg]] <- glb_allobs_df[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glbFeatsDerive[[tst]]$mapfn, args_lst)));
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]);
glb_date_vars <- NULL # or c("<date_var>")
glb_date_fmts <- list(); #glb_date_fmts[["<date_var>"]] <- "%m/%e/%y"
glb_date_tzs <- list(); #glb_date_tzs[["<date_var>"]] <- "America/New_York"
#grep("America/New", OlsonNames(), value=TRUE)
glbFeatsPrice <- c("startprice") #: bid._sp # NULL or c("<price_var>")
# Text Processing Step: custom modifications not present in txt_munge
glbFeatsText <- c("descr.my") # NULL #
Sys.setlocale("LC_ALL", "C") # For english## [1] "C/C/C/C/C/en_US.UTF-8"# Text Processing Step: universal modifications
glb_txt_munge_filenames_pfx <- "ebay_mytxt_"
# Text Processing Step: tolower
# Text Processing Step: removePunctuation (use custom transformer to replace with space ???)
# Text Processing Step: removeWords
glb_txt_stop_words <- list()
# Remember to use unstemmed words; Check stemming of "significant" words - any stopped words that should be stemmed with them ?
if (!is.null(glbFeatsText)) {
require(tm)
glb_txt_stop_words[["descr.my"]] <- sort(c(NULL
### bid._sp
# , setdiff(removePunctuation(stopwords("english")), "no")
# ,"ac"
# # cor.y.train == NA
# ,unlist(strsplit(paste(c(NULL
# ,"128gb,1st,32gb,3g,64gb,90,acceptable,activation,amount,average,bad,buttons,buy,came,camera,can,care,carrier"
# #,casing
# ,"certified,charge,charging,cleaned,clear,come,components,contain,corner,correctly,covered,customer,earbuds"
# ,"engraved,engraving,engravement" # somehow didn't show up in the cor.y.train == NA list
# ,"entire,except,fair,features,feel,fine,generation,get,gift,got,heavily,heavy,however,imei,include,inspected,invisible,invisibleshield"
# ,"ipad,ipads"
# ,"issues"
# #,items,
# ,"keyboard,lightning,listing,little,looks,lower"
# ,"manufacture,manufacturer"# somehow didn't show up in the cor.y.train == NA list
# ,"meaning,model,near,need,needs,nicks,opened,operational,otherwise"
# ,"person,personal"# somehow didn't show up in the cor.y.train == NA list
# ,"phone,photos,pics,plastic,port,professionally"
# ,"purchased,purchasing"# somehow didn't show up in the cor.y.train == NA list
# ,"quality,questions,read,ready"
# ,"receive,received"# somehow didn't show up in the cor.y.train == NA list
# ,"removed,replaced,retail,return,returns,runs"
# #,scratch,
# ,"scuffing,sealed,sell,seller,selling,shape,ship,shown,silver,since,sold,sound,spacegray,stock,sync,tablet,taken,technician,tests,third,time,touch,units,unlocked,week,wifi,without"
# ,"wrap" # somehow didn't show up in the cor.y.train == NA list
# ,"zagg"
# ), collapse=",")
# , "[,]")) #err.abs.fit.sum=26.869473 w/o items,scratch
#
# # cor.y.abs is low
# #,"always","comes","grade","moderate","protector"
### bid._sp
### !_sp
# freq == 1; keep "gold"
,"09","17","28","34","360","5c","5point1point1","511","6428"
,"7point9","7point9in","79in"
,"8point5","8point25","82510"
,"910","9510","9point7","9point75","97510","99"
,"a1314","a1430","abused","accept","accounts","across","actuuly","add","advised"
,"affects","am","ans","antenna","anti","anyone","anything","applied","applying"
,"area","arizona","att","attached"
,"backlight","backlit","backplate","barley"
,"beetle","beginning","bend","besides","between"
,"bidder","binder","bonus","book","boot","bound","brick","broke","bruises"
,"buyers"
,"capacity","causing","cherished","chrome","classes","closely"
,"condtion","conditon","confidence","considerable","consist","consistent"
,"consumer","contents","contidion","control"
,"couldnt"
,"cream","customize","cuts"
,"daily","date","daughter"
,"deactivated","decent","deep","defender","defense","degree","demonstration"
,"depicted","depress","desciption"
,"difficulty","digi","disclaimer","discoloration","distressed","divider"
,"dlxnqat9g5wt","dock","documents","done","durable","dust","duty"
,"each","effect","either","emblem","erased","esi","essentially","etch","etched"
,"euc","every","exact","exhibition","expires"
,"facing","faded","faint","february","film","final","five","flickers"
,"folding","forgot","forwarders"
,"freezes","freight"
,"functioanlity"
,"games","generic","genuine","glitter","goes","gray","grey","guide"
,"hairline","half","handstand","hdmi","here","high","higher"
,"hold","hole","hospital"
,"immaculate","impact"
,"inivisible","instead"
,"intended","interest","interior","international","internationally"
,"into","intro"
,"ios","isnt","itself","ive"
,"jack","july"
,"keyword","kids","kind","knicks"
,"l","largest","last","late","length","let","letters","level"
,"lifting","limited","literally","literature"
,"local","logic","long","longer","looping","loose","loss","lost"
,"mars"
,"mb292ll","mc707ll","mc916ll","mc991ll","md789ll","mf432ll"
,"mic","middle","mind","minimum","mixed"
,"myself"
,"neither","newer","non","none","nonviewing","nor","november"
,"occasional","oem","often","online","oped","outside","over"
,"padfolio","pairing","paperwork","past","period","pet","piece"
,"plate","played","plug"
,"poor","portfolio","portion","pouch"
,"preinstalled","pressure","price","proof","provided"
,"ranging","rather"
,"real","realized","reassemble","reboot","receipt","recently","red"
,"reflected","refunds","relisting","remote","repeat","reponding"
,"required"
,"reserve","reshaped","residue","respond","restarts","result"
,"reviewed"
,"ringer","roughly","rubber"
,"said","same","school","scratchs","screeb"
,"seamlessly","seem","seen","semi","send","september","serious"
,"shell","shipment","short","showroom"
,"sighs","site","size"
,"sleeve","slice"
,"smoke","smooth","smudge","sn"
,"softer","software","somewhat","soon"
,"space","sparingly","sparkiling","spec","special","speck","speed","speigen"
,"stains","standup","start","status","stopped","strictly"
,"subtle","sustained"
,"swappadotcom","swiped","swivel"
,"take","technical","tempered","texture"
,"thank","then","therefore","think","those","though"
,"toddler","topback","totally","touchy","toys"
,"tried","turn","typical"
,"u","university","unknown","untouched","uppser"
,"valid","vary","viewing","virtually"
,"want","wavy","website","whole","why","winning","worn"
,"zaag","zero","zombie","zoogue"
# cor.y.train == NA
,"account","applecare","download","expect","fourth","greeting","maybe"
,"plus","purposes","significant","title","volume"
# chisq.pval high (e.g. == 1);
# keep
# carrier.fctr: "sprint", "verizon"
# cellular.fctr: "3g", "4g",wifion
# color.fctr: "gold"
# prdl.descr.my.fctr:
# storage.fctr: "128gb"
,"2016"
,"acceptable","actual","amount","awesome"
,"beautiful","before","bent","best","blocked","blocks"
,"capable","converted"
,"find"
,"gift"
,"handled","handling","headphone"
,"im","information"
,"love"
,"march","meaning","means","medium","money"
,"necessary"
,"offer","once"
,"page","product","products"
,"second","seconds","should","silver","skin","skinned"
,"tape","thoroughly","twice"
,"user"
,"way","which"
# nzv.freqRatio high (e.g. >= glb_nzv_freqCut);
# keep
# carrier.fctr: "sprint", "tmobile", "verizon"
# cellular.fctr: "3g", "4g",wifionly
# color.fctr: "gold",spacegray
# condition.fctr:
# levels:
# "Used", "For parts or not working", "Manufacturer refurbished", "New", "New other (see details)", "Seller refurbished"
# stemmed tokens:
# manufactur
# prdl.descr.my.fctr: "ipad1",ipad3,ipad4,ipadair2,ipadmini2
# storage.fctr: "128gb"
,"14","2015","3","30","4","5","6","7","8","9","90","9point5","9point7in"
,"a1432","able","about","ac","activate","activated","activation"
,"additional","adult"
,"after"
,"ago"
,"air"
,"along","already","also"
,"another","answer"
,"appears","approved","april"
,"around"
,"asis","associated"
,"auction"
,"backside","bad","battery","because","bestbuy","bezel","blue","bluetooth"
,"board","body","both","bottom","bought"
,"bright"
,"bumps","buy","buying"
,"came","camera","cameras","cannot","cant","card","carrier","cellular"
,"changed","changing","check","chip","chips"
,"color","colors","company","complete","completely","components"
,"connect","connected","connector","connects","contain","contains"
,"corporate","correctly","couple"
,"customer"
,"data"
,"dead"
,"default","defaulting","definite","definitely"
,"delivered","demo","describe","described","details"
,"do","does","dont","down"
,"drop","dropped","drops"
,"due"
,"earbuds","easily","ebay","else","engraved","engravement","engraving","entire"
,"etc"
,"even","ever","everything"
,"except","exterior","extremely"
,"fantastic","fast","faulty","features","feel","feels","fine","fix","fixed"
,"flaw","flaws"
,"frame","framing"
,"geeksquad","general","get","got","guarantee","guaranteed","guarantees"
,"hand","handful","handset","happened","hard","hardly","heavy","home","however"
,"id","if","images","imperfections"
,"inside","inspected","install","installed","instructions","invisible"
,"iphone","iphones"
,"issue","issues"
,"itunes"
,"keyboard","know","known"
,"latest","lcd","least","leather","less"
,"life","lightening","lightning","like","line","lining"
,"liquid","liquidation"
,"logo","lot","lots","lower"
,"magnetic","make","manual","manuals","many","me","memory","missing"
,"model","models","moderate","more","most","mostly"
,"multiple","musthave"
,"my"
,"name","network","networks","nice","nick","nicks"
,"nonfunctioning","noticeable","now"
,"off","old","operated","operational","otherwise","outer","own","owned"
,"party","passcode","password"
,"performance","performs","person","personal","personalized"
,"phone","physical","physically"
,"pin","pixels"
,"placed","places"
,"port","possible"
,"pretty","pristine","problem","problems","properly"
,"purchase","purchased","purchasing"
,"quality","question","questions"
,"rarely"
,"reactivated","really","rear","receive","received","regular","removed"
,"repair","retail","retina"
,"rotate","rotation"
,"running","runs"
,"s","sale","sales","sameday","scrapes","scroll","setup","several"
,"shattered","shrinkwrap","shut","shuts"
,"side","sides","sim","single"
,"so","something","sometimes","sound"
,"speaker","specifics","spent"
,"sticker","stickers","storage","store","storing","stuck","stylus"
,"super","supply","sure","surface"
,"sync"
,"taken","technician","than","these","they","thin","third","three"
,"till","tiny"
,"too","took","touch","touching","touchscreen"
,"turns"
,"two"
,"unable","under","unnoticeable","unopened","unsealed","until"
,"unused","unusable"
,"up","update","updated","updates","upgrade","upgraded","upper"
,"usage","usually"
,"verified","verify"
,"wall","water","we","week","weeks", "were","what","whats","where","while"
,"wiped","without"
,"would"
,"wrap","wrapped","wrong"
,"x"
,"year","years","your"
,"zagg","zaaginvisibleshield"
#
### !_sp
))
}## Loading required package: tm
## Loading required package: NLP
##
## Attaching package: 'NLP'
##
## The following object is masked from 'package:ggplot2':
##
## annotate#orderBy(~term, glb_post_stem_words_terms_df_lst[[txt_var]][grep("^2", glb_post_stem_words_terms_df_lst[[txt_var]]$term), ])
#glb_allobs_df[glb_post_stem_words_terms_mtrx_lst[[txt_var]][, 6] > 0, glbFeatsText]
# To identify terms with a specific freq
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txt_var]], freq == 1)$term), collapse = ",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txt_var]], freq <= 2)$term), collapse = ",")
# To identify terms with a specific freq &
# are not stemmed together later OR is value of color.fctr (e.g. gold)
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txt_var]], (freq == 1) & !(term %in% c("blacked","blemish","blocked","blocks","buying","cables","careful","carefully","changed","changing","chargers","cleanly","cleared","connect","connects","connected","contains","cosmetics","default","defaulting","defective","definitely","describe","described","devices","displays","drop","drops","engravement","excellant","excellently","feels","fix","flawlessly","frame","framing","gentle","gold","guarantee","guarantees","handled","handling","having","install","iphone","iphones","keeped","keeps","known","lights","line","lining","liquid","liquidation","looking","lots","manuals","manufacture","minis","most","mostly","network","networks","noted","opening","operated","performance","performs","person","personalized","photograph","physically","placed","places","powering","pre","previously","products","protection","purchasing","returned","rotate","rotation","running","sales","second","seconds","shipped","shuts","sides","skin","skinned","sticker","storing","thats","theres","touching","unusable","update","updates","upgrade","weeks","wrapped","verified","verify") ))$term), collapse = ",")
#print(subset(glb_post_stem_words_terms_df_lst[[txt_var]], (freq <= 2)))
#glb_allobs_df[which(terms_mtrx[, 229] > 0), glbFeatsText]
# To identify terms with cor.y == NA
#orderBy(~-freq+term, subset(glb_post_stop_words_terms_df_lst[[txt_var]], is.na(cor.y)))
#paste(sort(subset(glb_post_stop_words_terms_df_lst[[txt_var]], is.na(cor.y))[, "term"]), collapse=",")
#orderBy(~-freq+term, subset(glb_post_stem_words_terms_df_lst[[txt_var]], is.na(cor.y)))
# To identify terms with low cor.y.abs
#head(orderBy(~cor.y.abs+freq+term, subset(glb_post_stem_words_terms_df_lst[[txt_var]], !is.na(cor.y))), 5)
# To identify terms with high chisq.pval
#subset(glb_post_stem_words_terms_df_lst[[txt_var]], chisq.pval > 0.99)
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txt_var]], (chisq.pval > 0.99) & (freq <= 10))$term), collapse=",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txt_var]], (chisq.pval > 0.9))$term), collapse=",")
#head(orderBy(~-chisq.pval+freq+term, glb_post_stem_words_terms_df_lst[[txt_var]]), 5)
#glb_allobs_df[glb_post_stem_words_terms_mtrx_lst[[txt_var]][, 68] > 0, glbFeatsText]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txt_var]][grep("^m", glb_post_stem_words_terms_df_lst[[txt_var]]$term), ])
# To identify terms with high nzv.freqRatio
#summary(glb_post_stem_words_terms_df_lst[[txt_var]]$nzv.freqRatio)
#paste0(sort(setdiff(subset(glb_post_stem_words_terms_df_lst[[txt_var]], (nzv.freqRatio >= glb_nzv_freqCut) & (freq < 10) & (chisq.pval >= 0.05))$term, c( "128gb","3g","4g","gold","ipad1","ipad3","ipad4","ipadair2","ipadmini2","manufactur","spacegray","sprint","tmobil","verizon","wifion"))), collapse=",")
# To identify obs with a txt term
#tail(orderBy(~-freq+term, glb_post_stop_words_terms_df_lst[[txt_var]]), 20)
#mydsp_obs(list(descr.my.contains="non"), cols=c("color", "carrier", "cellular", "storage"))
#grep("ever", dimnames(terms_stop_mtrx)$Terms)
#which(terms_stop_mtrx[, grep("ipad", dimnames(terms_stop_mtrx)$Terms)] > 0)
#glb_allobs_df[which(terms_stop_mtrx[, grep("16", dimnames(terms_stop_mtrx)$Terms)[1]] > 0), c(glb_category_var, "storage", txt_var)]
# To identify whether terms shd be synonyms
#orderBy(~term, glb_post_stop_words_terms_df_lst[[txt_var]][grep("^moder", glb_post_stop_words_terms_df_lst[[txt_var]]$term), ])
# term_row_df <- glb_post_stop_words_terms_df_lst[[txt_var]][grep("^came$", glb_post_stop_words_terms_df_lst[[txt_var]]$term), ]
#
# cor(glb_post_stop_words_terms_mtrx_lst[[txt_var]][glb_allobs_df$.lcn == "Fit", term_row_df$pos], glb_trnobs_df[, glb_rsp_var], use="pairwise.complete.obs")
# To identify which stopped words are "close" to a txt term
#sort(cluster_vars)
# Text Processing Step: stemDocument
# To identify stemmed txt terms
#glb_post_stop_words_terms_df_lst[[txt_var]][grep("condit", glb_post_stop_words_terms_df_lst[[txt_var]]$term), ]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txt_var]][grep("^con", glb_post_stem_words_terms_df_lst[[txt_var]]$term), ])
#glb_allobs_df[which(terms_stem_mtrx[, grep("use", dimnames(terms_stem_mtrx)$Terms)[[1]]] > 0), c(glb_id_var, "productline", txt_var)]
#glb_allobs_df[which(TfIdf_stem_mtrx[, 191] > 0), c(glb_id_var, glb_category_var, txt_var)]
#which(glb_allobs_df$UniqueID %in% c(11915, 11926, 12198))
# Text Processing Step: mycombineSynonyms
# To identify which terms are associated with not -> combine "could not" & "couldn't"
#findAssocs(glb_full_DTM_lst[[txt_var]], "not", 0.05)
# To identify which synonyms should be combined
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txt_var]][grep("^c", glb_post_stem_words_terms_df_lst[[txt_var]]$term), ])
chk_comb_cor <- function(syn_lst) {
# cor(terms_stem_mtrx[glb_allobs_df$.src == "Train", grep("^(damag|dent|ding)$", dimnames(terms_stem_mtrx)[[2]])], glb_trnobs_df[, glb_rsp_var], use="pairwise.complete.obs")
print(subset(glb_post_stem_words_terms_df_lst[[txt_var]], term %in% syn_lst$syns))
print(subset(get_corpus_terms(tm_map(glb_txt_corpus_lst[[txt_var]], mycombineSynonyms, list(syn_lst), lazy=FALSE)), term == syn_lst$word))
# cor(terms_stop_mtrx[glb_allobs_df$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])], glb_trnobs_df[, glb_rsp_var], use="pairwise.complete.obs")
# cor(rowSums(terms_stop_mtrx[glb_allobs_df$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])]), glb_trnobs_df[, glb_rsp_var], use="pairwise.complete.obs")
}
#chk_comb_cor(syn_lst=list(word="cabl", syns=c("cabl", "cord")))
#chk_comb_cor(syn_lst=list(word="damag", syns=c("damag", "dent", "ding")))
#chk_comb_cor(syn_lst=list(word="dent", syns=c("dent", "ding")))
#chk_comb_cor(syn_lst=list(word="use", syns=c("use", "usag")))
glb_txt_synonyms <- list()
glb_txt_synonyms[["descr.my"]] <- #NULL : default
list(NULL
### bid._sp
# , list(word="cabl", syns=c("cabl", "cord"))#err.abs.fit.sum=26.863220
# # , list(word="charger", syns=c("charg", "charger"))
# # , list(word="come", syns=c("came", "come"))
# # , list(word="dent", syns=c("dent", "ding"))
# # , list(word="damag", syns=c(#"bad", "blemish", "broken", "crack",
# # #defect has +ve cor, others have -ve cor
# # "damag", "dent", "ding",
# # #"scratch", "scuff", "tear", "wear",
# # NULL))
# # # combining damag with defect & dent results in higher err.abs.fit.sum=26.885899
# # # combining defect with dent in higher err.abs.fit.sum=26.894976
# # , list(word="defect", syns=c(#"bad", "blemish", "broken", "crack",
# # "defect", "dent", #"ding", ding has -ve cor, others have +ve cor
# # #"scratch", "scuff", "tear", "wear",
# # NULL))
# #, list(word="new", syns=c("brand")) ???
# # , list(word="scuff", syns=c("scuf", "scuff"))
# # , list(word="show", syns=c("show", "shown"))
# # , list(word="tablet", syns=c("tab", "tablet"))
### bid._sp
### !_sp
,list(word = "cabl", syns = c("cabl", "cord"))
,list(word = "cant", syns = c("cant", "cannot"))
,list(word = "descript", syns = c("descript", "discript"))
,list(word = "generat", syns = c("gen", "generat"))
,list(word = "ipadmini", syns = c("ipadmini", "mini"))
,list(word = "kept", syns = c("keep", "kept"))
,list(word = "know", syns = c("know", "known"))
,list(word = "lightn", syns = c("lighten", "lightn"))
,list(word = "passcod", syns = c("passcod", "password"))
,list(word = "photo", syns = c("photo", "photograph", "photos", "pic", "pictur"))
,list(word = "preown", syns = c("pre", "preown", "previous",
"previouslyus", "previouslyown"))
,list(word = "protector", syns = c("protect", "protector"))
,list(word = "scuff", syns = c("scuf", "scuff"))
,list(word = "tablet", syns = c("tab", "tablet"))
,list(word = "with", syns = c("w", "with"))
,list(word = "zagg", syns = c("zagg", "zaaginvisibleshield"))
### !_sp
)
if (length(glb_txt_synonyms) > 0) names(glb_txt_synonyms) <- glbFeatsText
# Text Processing Step: filterTerms
if (!is.null(glbFeatsText)) {
require(tm)
# options include: weightTf, myweightTflog1p, myweightTfsqrt, weightTfIdf, weightBM25
glb_txt_terms_control <- list(weighting = weightTfIdf # : default
# termFreq selection criteria across obs: default: list(global=c(1, Inf))
, bounds = list(global = c(1, Inf)) # bid._sp: list(global=c(3, Inf))
# default: c(3, Inf)
, wordLengths = c(1, Inf) # bid._sp: c(2, Inf)
)
}
glb_txt_cor_var <- glb_rsp_var # bid._sp: "startprice.log10.cut.fctr" # default: glb_rsp_var
# select one from c("union.top.val.cor", "top.cor", default: "top.val", "top.chisq", "sparse")
glbFeatsTextFilter <- "top.chisq"
glbFeatsTextTermsMax <- c(15) # bid._sp: c(20) # c(50) in (old) !_sp # default: rep(10, length(glbFeatsText))
names(glbFeatsTextTermsMax) <- glbFeatsText
# Text Processing Step: extractAssoc
glbFeatsTextAssocCor <- c(1.0) #bid._sp: c(0.4) #(old) !_sp: 0.2 #default: rep(1, length(glbFeatsText))
names(glbFeatsTextAssocCor) <- glbFeatsText
# Text Processing Step: extractPatterns (ngrams)
# Potential Enhancements
# "Seller refurbished" -> D.P.refurbished.seller ?
# "Like new" -> D.P.new.like ?
# "No scratches" -> D.P.scratch.no ?
glb_important_terms <- list()
# Remember to use stemmed terms
# Have to set it even if it is not used
glb_sprs_thresholds <- c(0.950) # Generates 8 terms
# Properties:
# numrows(glb_feats_df) << numrows(glb_fitobs_df)
# Select terms that appear in at least 0.2 * O(FP/FN(glb_OOBobs_df))
# numrows(glb_OOBobs_df) = 1.1 * numrows(glb_newobs_df)
names(glb_sprs_thresholds) <- glbFeatsText
if (glb_rsp_var_raw != glb_rsp_var)
glbFeatsExclude <- union(glbFeatsExclude,
glb_rsp_var_raw)
glbFctrMaxUniqVals <- 23 # default: 20
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer
glb_cluster <- TRUE # bid._sp:TRUE # default:FALSE
glb_cluster.seed <- 189 # or any integer
### !_sp
glb_cluster_entropy_var <- glb_rsp_var # c(glb_rsp_var, as.factor(cut(glb_rsp_var, 3)), default: NULL)
glbFeatsTextClusterVarsExclude <- FALSE # default FALSE
### bid._sp
# glb_cluster_entropy_var <- "sold" #"startprice.log10.cut.fctr"
# glbFeatsTextClusterVarsExclude <- TRUE # default FALSE
### bid._sp
glb_interaction_only_feats_lst <- list()
### bid._sp
glb_interaction_only_feats_lst[["carrier.fctr"]] <- "cellular.fctr"
glb_nzv_freqCut <- 19 # 19 is caret default
glb_nzv_uniqueCut <- 10 # 4 : bid._sp # 10 : caret::default
### bid._sp
glbRFESizes <- list()
# c(106, 111, 116, 120, 128) # bid0_sp
# c(8, 11, 16, 21, 32, 64, 128) # bid1_sp
# c(47,48,49,50,51,52,53,54) # no_sp
### bid1_nosp
# rfFuncs
#c(4, 8, 16, 32, 64, 82) # Accuracy@82 = 0.8317
#c(4, 8, 16, 32, 64, 73, 82, 91) # Accuracy@82 = 0.8325
#c(16, 32, 64, 73, 76, 78, 80, 81, 82, 83) # Accuracy@82 = 0.8333
# ldaFuncs
#c(8, 16, 32, 64, 67) # Accuracy@16 = 0.8519
#c(8, 12, 16, 20, 32, 64, 67) # Accuracy@12 = 0.8519
#c(8, 10, 12, 13, 14, 15, 16, 20, 67) # Accuracy@14 = 0.8526
### bid1_nosp
### bid0_nosp
#c(4, 8, 12, 16, 20, 32, 64, 87) # Accuracy@16 = 0.7877
#c(12, 14, 15, 16, 17, 18, 20, 87) # Accuracy@17 = 0.7894
#c(8, 12, 13, 14, 15, 16, 18, 20, 87) # Accuracy@14 = 0.7900 before removing outliers
#c(8, 12, 13, 14, 15, 16, 18, 20, 87) # Accuracy@15 = 0.7868
### bid0_nosp
#c(4, 8, 16, 32, 64, 66, 67, 68) # Accuracy@67/68 = 0.8271
#c(4, 8, 16, 32, 64, 66, 67, 68, 70) # Accuracy@70 = 0.8258
#c(4, 8, 16, 32, 64) # Accuracy@70 = 0.8258
#c(4, 8, 16, 32, 64, 67) # Accuracy@70 = 0.8258
glbRFESizes[["RFE.X"]] <- c(4, 8, 16, 32, 64, 67, 68, 69) # Accuracy@69/70 = 0.8258
glbRFESizes[["Final"]] <-
#c(4, 8, 16, 32, 64) # Accuracy@71 = 0.8250
#c(4, 8, 16, 32, 64, 66) # Accuracy@71 = 0.8250
#c(4, 8, 16, 32, 64, 66, 69) # Accuracy@71 = 0.8250
c(4, 8, 16, 32, 64, 66, 69, 70) # Accuracy@70/71 = 0.8250
# outliers identified by car::outlierTest
glbObsFitOutliers <- list()
### bid._sp
# c(NULL # default: NULL
# biddable == 0 & 1; err.abs.fit.sum=423.55172
# # outliers
# , 10813 # next 665 w/ rstudent=-5.091080; biddable=3.263257; err.abs.fit.sum=418.598755
# , 10666 # next 1727 w/ rstudent=-5.163517; biddable=4.293465; err.abs.fit.sum=414.093609
# , 11736 # next 780 w/ rstudent=-5.181343; biddable=5.670483; err.abs.fit.sum=401.817992
# # old biddable importance above this
# , 10781 # next 1323 w/ rstudent=-5.151062; biddable=13.30602; err.abs.fit.sum=396.393721
# #, 10091 # next 91 w/ rstudent=-4.444452; biddable=; err.abs.fit.sum=402.673715 (up)
# #, 10166 # next 560 w/ rstudent=-5.006795; biddable=; err.abs.fit.sum=401.759324 (up)
# #, 10281 # next 281 w/ rstudent=-4.245087; biddable=; err.abs.fit.sum=401.316926 (up)
# #, 10285 # next 285 w/ rstudent=-4.483190; biddable=; err.abs.fit.sum=402.608936 (up)
# #, 10446 # next 445 w/ rstudent=-4.663418; biddable=; err.abs.fit.sum=403.074523 (up)
# #, 10542 # next 1323 w/ rstudent=-5.214517; biddable=; err.abs.fit.sum=401.04205 (up)
# #, 10543 # next 1323 w/ rstudent=-5.214517; biddable=; err.abs.fit.sum=401.04205 (up)
# #, 10561 # next 542 w/ rstudent=-4.736154; biddable=; err.abs.fit.sum=401.56198 (up)
# #, 10631 # next 166 w/ rstudent=-5.073048; biddable=; err.abs.fit.sum=401.556788 (up)
# #, 11330 # next 630 w/ rstudent=-5.117659; biddable=; err.abs.fit.sum=401.732597 (up)
# , 10091, 10166, 10281, 10285, 10446, 10542, 10543, 10561, 10631, 11330
# # biddable=18.93923; err.abs.fit.sum=359.388769
# , 10330 #biddable=19.06084; err.abs.fit.sum=355.895702
# , 10402 #biddable= 0.0 ; err.abs.fit.sum=351.315181
# , 10438 #biddable= 0.0 ; err.abs.fit.sum=347.821527
# , 10624 #biddable= 0.0 ; err.abs.fit.sum=343.724904
# , 10659 #biddable= 0.0 ; err.abs.fit.sum=331.873603
# , 11323 #biddable=10.45901; err.abs.fit.sum=324.929562
# , 11422 #biddable= 0.0 ; err.abs.fit.sum=334.839805 (up)
# biddable == 0; err.abs.fit.sum=26.713317
# , 11448 # outliers; next is 858 w/ rstudent=-5.855132; err.abs.fit.sum=24.212800
# , 11583 # outliers; next is 856 w/ rstudent=-4.792849; err.abs.fit.sum=22.164035
# , 11581 # outliers; next is 743 w/ rstudent=-4.005054; err.abs.fit.sum=18.842901
# , 10837 # outliers; next is 336 w/ rstudent=-5.279215; err.abs.fit.sum=18.124560
# , 11442 # outliers; next is 904 w/ rstudent=-4.474844; err.abs.fit.sum=15.533211
# , 11697 # outliers; next is 874 w/ rstudent=-3.678664; err.abs.fit.sum=13.829375
# , 10799 # .hatvalues == 1; total 8; iPadmini#1; err.abs.fit.sum=13.807283
# , 10017 # .hatvalues == 1; total 7; iPad3#1; err.abs.fit.sum=14.620782 (up)
# , 10027, 10859 # .hatvalues == 1; total 7; iPad1#1; err.abs.fit.sum=14.570246 (up)
# , 10332 # .hatvalues == 1; total 7; iPad4#1; err.abs.fit.sum=13.706467
# , 11759 # .hatvalues == 1; total 6; iPadAir2#1; err.abs.fit.sum=13.643043
# , 10675 # .hatvalues == 1; total 5; iPadAir#1; err.abs.fit.sum=13.623787
# , 11119 # .hatvalues == 1; total 4; iPadmini3#1; err.abs.fit.sum=NA
# , 10017, 10027, 10859 # .hatvalues == 1; total 1; iPad3#1 & iPad1#1; err.abs.fit.sum=13.438903
# biddable == 1; err.abs.fit.sum=361.78243
# , 10813 # outliers; next is 665 w/ rstudent=-5.021180; err.abs.fit.sum=356.83424
# , 10666 # outliers; next is 808 w/ rstudent=-4.764126; err.abs.fit.sum=352.46437
# , 11736 # outliers; next is 665 w/ rstudent=-4.614022; err.abs.fit.sum=348.59977
# , 10542 # outliers; next is 665 w/ rstudent=-4.654923; err.abs.fit.sum=344.18546
# , 11330 # outliers; next is 327 w/ rstudent=-4.628972; err.abs.fit.sum=336.12636
# , 10561 # outliers; next is 56 w/ rstudent=-4.612970; err.abs.fit.sum=329.50309
# , 10166 # outliers; next is 318 w/ rstudent=-4.717238; err.abs.fit.sum=318.50562
# , 10543 # outliers; next is 464 w/ rstudent=-4.811116; err.abs.fit.sum=314.32801
# , 10285 # outliers; next is 21 w/ rstudent=-4.850822; err.abs.fit.sum=310.19008
# #, 10091 # outliers; next is 464 w/ rstudent=-4.941448; err.abs.fit.sum=312.94069 (up)
# #, 10781 # outliers; next is 250 w/ rstudent=-4.793502; err.abs.fit.sum=313.03867 (up)
# , 10446 # outliers; next is 371 w/ rstudent=-4.787578; err.abs.fit.sum=307.15681
# , 10631 # outliers; next is 165 w/ rstudent=-4.130356; err.abs.fit.sum=303.34549
# #, 10330 # outliers; next is 217 w/ rstudent=-4.067684; err.abs.fit.sum=312.75121 (up)
# #, 10402 # outliers; next is 388 w/ rstudent=-4.067684; err.abs.fit.sum=311.84516 (up)
# #, 10659 # outliers; next is 128 w/ rstudent=-3.982911; err.abs.fit.sum=311.84516 (up)
# , 10091, 10781, 10330, 10402, 10659#, 10281 outliers; err.abs.fit.sum=282.381827; iPad4#0=13.806011; iPad4#1=7.799398
# #, 10281 # outliers; next is NA w/ rstudent=NA; err.abs.fit.sum=287.147331 (up); iPad4#0=14.372770; iPad4#1=4.591408
# #, 10624 # outliers; ignored along with 10281 err.abs.fit.sum=289.116467 (up); iPad4#0=; iPad4#1=
# #, 10624 # outliers; ignored w/o 10281 err.abs.fit.sum=286.415040 (up); iPad4#0=; iPad4#1=
# #, 10636 # hatvalues==1; next is 11652; err.abs.fit.sum=290.50254 (up)
# , 11652 # hatvalues==1; next is 10636; err.abs.fit.sum=282.183867
# #err.abs.fit.sum=282.227249
# )
### bid._sp
### no_sp
glbObsFitOutliers[["RFE.X"]] <- c(NULL
# is.na(RFE.X.glm$.dffits)
, 11233
)
glbObsFitOutliers[["CSM.X"]] <- c(NULL
# is.na(.rstudent)
, 11233, 11544
# .hatvalues >= 0.99
# -38,167,642 < minmax(.rstudent) < 49,649,823
#, 10241, 10573, 10834, 10930, 11078, 11596, 11807, 11846
)
# glbObsFitOutliers[["CSM2.X"]] <- c(NULL
# # is.na(.rstudent)
# , 11233, 11250, 11452, 11544
# # .hatvalues >= 0.99
# # -51,891,504 < minmax(.rstudent) < 44,019,269
# , 11504, 11713, 11760, 11814 + 2 more
# )
### no_sp
### bid0_nosp
# glbObsFitOutliers[["RFE.X"]] <- c(NULL
# # is.na(RFE.X.glm$.dffits)
# , 10026, 11172, 11357, 11767
# )
# glbObsFitOutliers[["CSM.X"]] <- c(NULL
# # is.na(.dffits)
# , 10045, 10089, 10918, 11852 # iPad2#1
# , 10852, 11340, 11464 # iPadmini3#0
# , 10014, 10025, 10218, 10287, 10382, 10789, 11005, 11404, 11497, 11580, 11763, 11835 # c("Unknown#0", "iPad1#0", "iPad3#0", "iPadAir#0", "iPadAir2#0", "iPadmini#0")
# , 10155, 10362, 10746, 11029, 11451, 11724, 11759, 11799, 11807 # c("Unknown#1", "iPad1#1", "iPad2#0", "iPad3#1", "iPad4#0", "iPadAir#1", "iPadAir2#1", "iPadmini#1", "iPadmini3#1")
# , 11001 # Unknown#1
#
# # min(.rstudent) <= -121446236
# , 11760 # iPadAir2#0
# , 11751 # iPad2#1
# # max(.rstudent) >= 70875386
# , 11817 # Unknown#1
# , 10763 # iPad1#0
# , 10875 # iPad1#1
#
# # .hatvalues >= 0.99
# )
# c(NULL
# # is.na(CSM.X.glm$.dffits)
# ,10026, 10089, 10218, 10382, 10918, 11005
# # CSM.X.glm$.hatvalues > 0.99
# ,10014, 11444, 11448, 11455, 11835, 11852
# # CSM.X.glm$.rstudent <= -43969313 & >= 44111144
# ,10675, 11272
# )
### bid0_nosp
# influence.measures: car::outlier; rstudent; dffits; hatvalues; dfbeta; dfbetas
#mdlId <- "Final.glm";
#print(outliers_df <- data.frame(.Bonf.p=outliers$bonf.p))
#mdlId <- "RFE.X.glm"; obs_df <- fitobs_df
#mdlId <- "Final.glm"; obs_df <- trnobs_df
#mdlId <- "CSM2.X.glm"; obs_df <- fitobs_df
#print(outliers <- car::outlierTest(glb_models_lst[[mdlId]]$finalModel))
#mdlIdFamily <- paste0(head(unlist(str_split(mdlId, "\\.")), -1), collapse="."); obs_df <- dplyr::filter_(obs_df, interp(~(!(var %in% glbObsFitOutliers[[mdlIdFamily]])), var = as.name(glb_id_var))); model_diags_df <- cbind(obs_df, data.frame(.rstudent=stats::rstudent(glb_models_lst[[mdlId]]$finalModel)), data.frame(.dffits=stats::dffits(glb_models_lst[[mdlId]]$finalModel)), data.frame(.hatvalues=stats::hatvalues(glb_models_lst[[mdlId]]$finalModel)));print(summary(model_diags_df[, c(".rstudent",".dffits",".hatvalues")])); table(cut(model_diags_df$.hatvalues, breaks=c(0.00, 0.98, 0.99, 1.00)))
#print(subset(model_diags_df, is.na(.rstudent))[, glb_id_var])
#print(subset(model_diags_df, is.na(.dffits))[, glb_id_var])
#print(model_diags_df[which.min(model_diags_df$.dffits), ])
#print(subset(model_diags_df, .hatvalues > 0.99)[, glb_id_var])
#dffits_df <- merge(dffits_df, outliers_df, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#dffits_df <- merge(dffits_df, glb_fitobs_df, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#subset(dffits_df, !is.na(.Bonf.p))
#mdlId <- "CSM.X.glm"; vars <- myextract_actual_feats(row.names(orderBy(reformulate(c("-", paste0(mdlId, ".importance"))), myget_feats_importance(glb_models_lst[[mdlId]]))));
#model_diags_df <- glb_get_predictions(model_diags_df, mdlId,glb_rsp_var_out)
#obs_ix <- row.names(model_diags_df) %in% names(outliers$rstudent)[1]
#obs_ix <- which(is.na(model_diags_df$.rstudent))
#obs_ix <- which(is.na(model_diags_df$.dffits))
#myplot_parcoord(obs_df=model_diags_df[, c(glb_id_var, glb_category_var, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, paste0(glb_rsp_var_out, mdlId), vars[1:min(20, length(vars))])], obs_ix=obs_ix, id_var=glb_id_var, category_var=glb_category_var)
#model_diags_df[row.names(model_diags_df) %in% names(outliers$rstudent)[c(1:2)], ]
#ctgry_diags_df <- model_diags_df[model_diags_df[, glb_category_var] %in% c("Unknown#0"), ]
#myplot_parcoord(obs_df=ctgry_diags_df[, c(glb_id_var, glb_category_var, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indep_vars[1:20])], obs_ix=row.names(ctgry_diags_df) %in% names(outliers$rstudent)[1], id_var=glb_id_var, category_var=glb_category_var)
#table(glb_fitobs_df[model_diags_df[, glb_category_var] %in% c("iPad1#1"), "startprice.log10.cut.fctr"])
#glb_fitobs_df[model_diags_df[, glb_category_var] %in% c("iPad1#1"), c(glb_id_var, "startprice")]
# No outliers & .dffits == NaN
#myplot_parcoord(obs_df=model_diags_df[, c(glb_id_var, glb_category_var, glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indep_vars[1:10])], obs_ix=seq(1:nrow(model_diags_df))[is.na(model_diags_df$.dffits)], id_var=glb_id_var, category_var=glb_category_var)
#dffits_ctgry_df <- subset(dffits_df, prdl.descr.my.fctr %in% c("Unknown#0"))
#myplot_parcoord(obs_df=dffits_ctgry_df[, c(glb_id_var, glb_category_var, ".dffits", ".Bonf.p", glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indep_vars[1:5])], obs_ix=seq(1:nrow(dffits_ctgry_df))[!is.na(dffits_ctgry_df$.Bonf.p)], id_var=glb_id_var, category_var=glb_category_var)
#
#car::influenceIndexPlot(glb_models_lst[["RFE.X.glm"]]$finalModel, id.n=3)
# myplot_parcoord(obs_df=glb_fitobs_df[, c(glb_id_var, glb_rsp_var,
# "startprice.log10.predict.RFE.X.glmnet",
# indep_vars[1:5])], obs_ix=hatobs_ix, id_var=glb_id_var)
# myplot_parcoord(x=glb_fitobs_df[, c(glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet",
# indep_vars[1:2])], obs_ix=hatobs_ix)
# hatvals <- hatvalues(glb_models_lst[["RFE.X.glm"]]$finalModel)
# hatobs_ix <- which(hatvals == max(hatvals))
# MASS::parcoord(x=glb_fitobs_df[, c(glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet",
# indep_vars[1:2])], var.label=TRUE)
#plot(hatvalues(glb_models_lst[["RFE.X.glm"]]$finalModel), type = "h")
#glb_fitobs_df[which(row.names(glb_fitobs_df) %in% c("972")), c(glb_id_var, glb_rsp_var, glb_rsp_var_raw, "sold", glb_category_var)]
#all.equal(glb_models_lst[[glb_sel_mdl_id]], glb_models_lst[[glb_fin_mdl_id]])
glbObsTrnOutliers <- list()
#car::outlierTest(glb_models_lst[["RFE.X.glm"]]$finalModel)
#glb_trnobs_df[which(row.names(glb_fitobs_df) %in% c("972")), c(glb_id_var, glb_rsp_var, glb_rsp_var_raw, "sold", glb_category_var)]
glb_models_lst <- list(); glb_models_df <- data.frame()
# Regression
if (glb_is_regression) {
glbMdlMethods <- c(NULL
# deterministic
#, "lm",
, "glm"
#, "bayesglm" # crashing w/ parallel processing
, "glmnet", "rpart"
# non-deterministic
, "gbm", "rf"
# Unknown
#, "nnet" , "avNNet" # predicts 1 for all obs in bid0_sp # runs 25 models per cv sample for tunelength=5
, "svmLinear", "svmLinear2"
#, "svmPoly" # crashing w/ parallel processing #, "svmPoly" runs 75 models per cv sample for tunelength=5
#, "svmRadial" # crashing w/ parallel processing
, "earth"
#, "bagEarth" # Takes a long time
)
} else
# Classification - Add ada,bagEarth (auto feature selection)
if (glb_is_binomial)
glbMdlMethods <- c(NULL
# deterministic
, "bagEarth" # Takes a long time
, "glm", "bayesglm", "glmnet"
, "nnet"
, "rpart"
# non-deterministic
, "gbm"
, "avNNet" # runs 25 models per cv sample for tunelength=5
, "rf"
# Unknown
, "lda", "lda2"
# svm models crash when predict is called -> internal to kernlab it should call predict without .outcome
# , "svmLinear", "svmLinear2", "svmPoly" #, "svmPoly" runs 75 models per cv sample for tunelength=5
# , "svmRadial"
, "earth"
) else
glbMdlMethods <- c("rpart", "rf", "gbm")
glb_mdl_family_lst <- list(); glb_mdl_feats_lst <- list()
# family: Choose from c("RFE.X", "CSM.X", "All.X", "Best.Interact")
# methods: Choose from c(NULL, <method>, glbMdlMethods)
glb_mdl_family_lst[["CSM.X"]] <- setdiff(glbMdlMethods, c("lda", "lda2")) # crashing due to category:.clusterid ??? #c("glmnet", "glm") # non-NULL list is mandatory
# glb_mdl_family_lst[["CSM2.X"]] <- c("glmnet", "glm") # non-NULL list is mandatory
glb_mdl_family_lst[["RFE.X"]] <- c("glmnet", "glm") # # setdiff(glbMdlMethods, c("lda", "lda2")) # crashing due to category:.clusterid #non-NULL list is mandatory
glb_mdl_family_lst[["All.X"]] <- "glmnet" # non-NULL list is mandatory
glb_mdl_family_lst[["Best.Interact"]] <- "glmnet" # non-NULL list is mandatory
### bid1_sp
# glb_mdl_family_lst[["CSM.X"]] <- "glmnet"
# glb_mdl_feats_lst[["CSM.X"]] <- c(NULL
# # from RFE.X
# , "startprice.dgt1.is9", "startprice.dcm2.is9", "startprice.dcm1.is9", "startprice.dgt2.is9"
# #, "condition.fctr"
# , "prdl.descr.my.fctr", "color.fctr"
# #, "D.ratio.weight.sum.wrds.n"
# , "cellular.fctr", "cellular.fctr:carrier.fctr"
#
# # from RFE.X.Interact
# , "cellular.fctr:prdl.descr.my.fctr", "cellular.fctr:startprice.dgt2.is9", "cellular.fctr:startprice.dgt1.is9", "cellular.fctr:color.fctr"
# , "cellular.fctr:condition.fctr" # RMSE up with keeping condition.fctr in the model
# # RMSE & R.sq up with removing condition.fctr from the model
# , "cellular.fctr:D.ratio.weight.sum.wrds.n"
# )
### bid1_sp
### !_sp
# glb_mdl_family_lst[["CSM.X"]] <- "glmnet"
# glb_mdl_feats_lst[["CSM.X"]] <- c(NULL
# , "D.ratio.sum.TfIdf.wrds.n", "D.TfIdf.sum.stem.stop.Ratio",
# , "D.npnct15.log", "D.npnct03.log", "D.wrds.n.log", "D.chrs.n.log")
# indep_vars <- union(setdiff(indep_vars, interact_vars_vctr),
# paste(glb_category_var, interact_vars_vctr,
# sep=ifelse(grepl("\\.fctr", glb_category_var), "*", ".fctr*")))
# indep_vars <- union(setdiff(indep_vars,
# c("startprice.log.diff", "startprice.unit9", "biddable", "cellular.fctr", "carrier.fctr")),
# c("startprice.log.diff*biddable", "startprice.unit9*biddable", "cellular.fctr*carrier.fctr"))
### !_sp
# dAFeats.CSM.X %<d-% c(NULL
# # Intearction feats up to varImp(RFE.X.glmnet) >= 50
# , "spdiff.cut.fctr*biddable"
# #, "spdiff.cut.fctr*prdl.my.fctr"
# #, "spdiff.cut.fctr*storage.fctr"
# #, "spdiff.cut.fctr*condition.fctr"
# #, "spdiff.cut.fctr*D.chrs.pnct11.n.log"
# , setdiff(myextract_actual_feats(predictors(rfe_fit_results)), c(NULL
# , "spdiff.cut.fctr"
# , "biddable"
# #, "prdl.my.fctr"
# #, "storage.fctr"
# #, "condition.fctr"
# #, "D.chrs.pnct11.n.log"
# ))
# )
# glb_mdl_feats_lst[["CSM.X"]] <- "%<d-% dAFeats.CSM.X"
dAFeats.CSM.X %<d-% c(NULL
, "spdiff.cut.fctr*biddable"
#, "spdiff.cut.fctr*sprice.d20nexp"
#, "spdiff.cut.fctr*prdl.my.fctr:.clusterid.fctr"
, setdiff(myextract_actual_feats(predictors(rfe_fit_results)), c(NULL
, "spdiff.cut.fctr"
, "biddable"
#, "sprice.d20nexp"
#, "prdl.my.fctr", ".clusterid.fctr"
))
)
glb_mdl_feats_lst[["CSM.X"]] <- "%<d-% dAFeats.CSM.X"
# dAFeats.CSM2.X %<d-% c(NULL
# , "spdiff.cut.fctr*biddable"
# , "spdiff.cut.fctr*sprice.d20nexp"
# , "spdiff.cut.fctr*prdl.my.fctr"
# #, "spdiff.cut.fctr*prdl.my.fctr:.clusterid.fctr"
# , setdiff(myextract_actual_feats(predictors(rfe_fit_results)), c(NULL
# , "spdiff.cut.fctr"
# , "biddable"
# , "sprice.d20nexp"
# , "prdl.my.fctr"
# #, "prdl.my.fctr", ".clusterid.fctr"
# ))
# )
# glb_mdl_feats_lst[["CSM2.X"]] <- "%<d-% dAFeats.CSM2.X"
# Check if interaction features make fit better
# Check if tuning parameters make fit better
glb_tune_models_df <- data.frame()
#RFE.X.avNNet
### bid0_sp
# size=[1] 3 5 7 9; decay=[0] 1e-04 0.001 0.01 0.1; bag=[FALSE]; RMSE=1.3300906
### bid1_sp
# size=[1] 3 5 7 9; decay=0 0.0001 [0.001] 0.01 0.1; bag=[FALSE]; RMSE=0.9285472
### bid0&1_sp
#RFE.X.bagEarth
### bid0_sp
#RFE.X.bagEarth degree=[1]; nprune=[33]; RMSE=0.1507259
### bid1_sp
#RFE.X.bagEarth degree=[1]; nprune=[32]; RMSE=0.6379639
#RFE.X.bagEarth degree=[1] 2 3; nprune=8 16 32 64 [128]; RMSE=0.6334405
#RFE.X.bagEarth degree=1 [2]; nprune=16 32 64 128 [256]; RMSE=0.6211924
#RFE.X.bagEarth degree=1 [2]; nprune=64 128 200 225 [256]; RMSE=0.6320776 (up)
#RFE.X.bagEarth degree=[1] 2; nprune=64 128 225 256 [275]; RMSE=0.640644 (up)
#RFE.X.bagEarth degree=1 [2] 3; nprune=64 128 200 [256] 300; RMSE=0.6496039 (up)
#RFE.X.bagEarth degree=1 [2] 3; nprune=32 64 128 256 [512]; RMSE=0.6404529 (up)
#RFE.X.bagEarth degree=1 [2] 3; nprune=64 128 256 512 [1024]; RMSE=0.6486663 (up)
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
# ,data.frame(method = "bagEarth", parameter = "nprune", vals = "256")
# ,data.frame(method = "bagEarth", parameter = "degree", vals = "2")
# ))
### bid0&1_sp
### bid0_sp
#RFE.X.earth degree=[1]; nprune=2 [9] 17 25 33; RMSE=0.1334478
### bid0_sp
#RFE.X.gbm
### bid0_sp
# shrinkage=[0.1]; n.trees=50 100 150 [200] 250; RMSE=0.2062651
# shrinkage=0.00 0.05 0.10 0.15 [0.20]; n.trees=50 [100] 150 200 250; interaction.depth=1 [2] 3 4 5; n.minobsinnode=[10]; RMSE=0.2019453
# shrinkage=0.00 0.05 [0.10] 0.15 0.20; n.trees=50 100 150 200 [250]; interaction.depth=[1] 2 3 4 5; n.minobsinnode=[10]; RMSE=0.2008313
# shrinkage=0.05 [0.10] 0.15 0.20 0.25; n.trees=100 150 200 [250] 300; interaction.depth=[1] 2 3 4 5; n.minobsinnode=[10]; RMSE=0.2008313
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
# ,data.frame(method = "gbm", parameter = "shrinkage", min = 0.05, max = 0.25, by = 0.05)
# ,data.frame(method = "gbm", parameter = "n.trees", min = 100, max = 300, by = 50)
# ,data.frame(method = "gbm", parameter = "interaction.depth", min = 1, max = 5, by = 1)
# ,data.frame(method = "gbm", parameter = "n.minobsinnode", min = 10, max = 10, by = 10)
# #seq(from=0.05, to=0.25, by=0.05)
# ))
### bid1_sp
# shrinkage=[0.1]; n.trees=50 100 150 200 [250]; interaction.depth=1 2 3 4 [5]; n.minobsinnode=[10]; RMSE=0.5054172
# shrinkage=0.03 [0.04] 0.05 0.06 0.07; n.trees=100 [150] 200 250 300; interaction.depth=2 3 4 5 [6]; n.minobsinnode=6 [8] 10 12 14; RMSE=0.5036430
# shrinkage=0.03 [0.04] 0.05 0.06 0.07; n.trees=100 150 [200] 250 300; interaction.depth=3 4 5 [6] 7; n.minobsinnode=6 8 [10] 12 14; RMSE=0.502774
# shrinkage=0.04; n.trees=200; interaction.depth=6; n.minobsinnode=10; RMSE=0.502774
# shrinkage=[0.05] 0.10 0.15 0.20 0.25; n.trees=100 [150] 200 250 300; interaction.depth=2 3 [4] 5 6; n.minobsinnode=[10]; RMSE=0.5058678 (up)
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
# ,data.frame(method = "gbm", parameter = "shrinkage", vals = "0.04")
# ,data.frame(method = "gbm", parameter = "n.trees", vals = "200")
# ,data.frame(method = "gbm", parameter = "interaction.depth", vals = "6")
# ,data.frame(method = "gbm", parameter = "n.minobsinnode", vals = "10")
# ))
### bid0&1_sp
#RFE.X.glmnet
### bid1_sp
# alpha=0.100 [0.325] 0.550 0.775 1.000; lambda=0.0005232693 0.0024288010 0.0112734954 [0.0523269304] 0.2428800957; RMSE=0.6164891
### bid1_nosp
# alpha=0.325 0.550 0.775 0.8875 [1.000]; lambda=9.858855e-05 [0.0001971771] 0.0009152152 0.0042480525 0.0197177130; Accuracy=0.8455510
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
# ,data.frame(method = "glmnet", parameter = "alpha", vals = "0.550 0.775 0.8875 0.94375 1.000")
# ,data.frame(method = "glmnet", parameter = "lambda", vals = "9.858855e-05 0.0001971771 0.0009152152 0.0042480525 0.0197177130")
# ))
#RFE.X.nnet
### bid0_sp
# size=[1] 3 5 7 9; decay=[0] 1e-04 0.001 0.01 0.1; RMSE=1.3300906
### bid1_sp
# size=1 3 5 7 [9]; decay=0e+00 1e-04 1e-03 1e-02 [1e-01]; RMSE=0.9289109
# size=3 5 [7] 9 11; decay=0.0001 0.001 0.01 [0.1] 0.2; RMSE=0.9287422
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
# ,data.frame(method = "nnet", parameter = "size", vals = "3 5 7 9 11")
# ,data.frame(method = "nnet", parameter = "decay", vals = "0.0001 0.0010 0.0100 0.1000 0.2000")
# ))
### bid0&1_sp
#RFE.X.rf # Don't bother; results are not deterministic
### bid0_sp
# mtry=2 35 [68] 101 134; RMSE=0.1331992
# mtry=2 35 68 [101] 134; RMSE=0.1339974
### bid0_sp
### no_sp
# mtry=2 41 81 121 [161]; Accuracy=0.8398314
# mtry=41 81 [121] 161 181; Accuracy=0.8282403
### no_sp
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
# ,data.frame(method = "rf", parameter = "mtry", vals = "2 5 9 13 17")
# ))
#RFE.X.rpart
### bid0_sp
# cp=0.020 [0.025] 0.030 0.035 0.040; RMSE=0.1770237
### bid1_sp
# cp=0.001 [0.003] 0.005 0.007 0.009; RMSE=0.5186586
### bid0_nosp
# cp=0.004347826 [0.008695652] 0.017391304 0.021739130 0.034782609; Accuracy=0.8120340
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
# ,data.frame(method = "rpart", parameter = "cp", vals = "0.004347826 0.008695652 0.017391304 0.021739130 0.034782609")
# ))
#RFE.X.svmLinear
### bid0_sp
# C=[1]; RMSE=0.1374094
# C=1e-02 [0.1] 5e-01 1e+00 2e+00 3e+00 4e+00 1e+01 1e+02; RMSE=0.1271318
# C=0.01 0.05 [0.10] 0.50 1.00 2.00 3.00 4.00; RMSE=0.1271318; 0.1296718
### bid1_sp
# C=[1]; RMSE=0.6614060
# C=1e-02 [1e-01] 1e+00 1e+01 1e+02; RMSE=0.6373977
# C=[0.05] 0.10 0.50 1.00 10.00; RMSE=0.6324697
# C=0.01 [0.05] 0.10 0.50 1.00; RMSE=0.6324697
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
# ,data.frame(method = "svmLinear", parameter = "C", vals = "0.01 0.05 0.1 0.5 1")
# ))
### bid0&1_sp
#RFE.X.svmLinear2
### bid0_sp
# cost=[0.25] 0.50 1.00 2.00 4.00; RMSE=0.1276354
# cost=0.0625 0.1250 [0.25] 0.50 1.00; RMSE=0.1276354
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
# ,data.frame(method = "svmLinear2", parameter = "cost", vals = "0.0625 0.125 0.25 0.5 1")
### bid1_sp
# cost=[0.25] 0.50 1.00 2.00 4.00; RMSE=0.6483622
# cost=[0.0625] 0.1250 0.25 0.50 1.00; RMSE=0.6335311
# cost=0.0312 [0.0625] 0.1250 0.25 0.50; RMSE=0.6335311
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
# ,data.frame(method = "svmLinear2", parameter = "cost", vals = "0.0312 0.0625 0.125 0.25 0.50")
# ))
### bid0&1_sp
#RFE.X.svmPoly
### bid0_sp
# degree=[1] 2 3; scale=0.001 0.01 [0.1] 1 10; C=0.25 0.50 1.00 [2.00] 4.00; RMSE=0.1276130
# degree=[1] 2 3 4 5; scale=0.01 0.05 [0.1] 0.5 1; C=0.50 1.00 [2.00] 3.00 4.00; RMSE=0.1276130
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
# ,data.frame(method="svmPoly", parameter="degree", min=1, max=5, by=1) #seq(1, 5, 1)
# ))
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
# ,data.frame(method="svmPoly", parameter="scale", vals="0.01, 0.05, 0.1, 0.5, 1")
# ,data.frame(method="svmPoly", parameter="C", vals="0.50, 1.00, 2.00, 3.00, 4.00")
# ))
### bid0_sp
#RFE.X.svmRadial
### bid0_sp
# sigma=[0.08674323]; C=0.25 0.50 1.00 [2.00] 4.00; RMSE=0.1614957
### bid0_sp
#data.frame(parameter="mtry", min=080, max=100, by=10),
#glb_to_sav(); all.equal(sav_models_df, glb_models_df)
#glb_models_df <- subset(sav_models_df, id != "RFE.X.gbm"); print(sort(glb_models_df$id))
glb_preproc_methods <- # NULL
### bid0_sp
# c("YeoJohnson", "center.scale",
# # crashes with train: all the RMSE metric values are missing
# # probably due to interaction vars
# "range", "pca", "ica",
# "spatialSign")
### bid0_sp
### bid1_sp
# c("YeoJohnson", "center.scale", "range", "pca", "ica", "spatialSign")
### bid1_sp
c("YeoJohnson", "center.scale", "range", "pca", "ica", "spatialSign")
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<col_name>")
glbMdlMetric_terms <- NULL # or matrix(c(
# 0,1,2,3,4,
# 2,0,1,2,3,
# 4,2,0,1,2,
# 6,4,2,0,1,
# 8,6,4,2,0
# ), byrow=TRUE, nrow=5)
glbMdlMetricSummary <- NULL # or "<metric_name>"
glbMdlMetricMaximize <- NULL # or FALSE (TRUE is not the default for both classification & regression)
glbMdlMetricSummaryFn <- NULL # or function(data, lev=NULL, model=NULL) {
# confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
# #print(confusion_mtrx)
# #print(confusion_mtrx * glbMdlMetric_terms)
# metric <- sum(confusion_mtrx * glbMdlMetric_terms) / nrow(data)
# names(metric) <- glbMdlMetricSummary
# return(metric)
# }
glb_rcv_n_folds <- 3 # or NULL
glb_rcv_n_repeats <- 3 # or NULL
glb_clf_proba_threshold <- NULL # 0.5
# Model selection criteria
if (glb_is_regression)
glbMdlMetricsEval <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit")
#glbMdlMetricsEval <- c("min.RMSE.fit", "max.R.sq.fit", "max.Adj.R.sq.fit")
if (glb_is_classification) {
if (glb_is_binomial)
glbMdlMetricsEval <-
c("max.Accuracy.OOB", "max.AUCROCR.OOB", "max.AUCpROC.OOB", "min.aic.fit", "max.Accuracy.fit") else
glbMdlMetricsEval <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}
# select from NULL [no ensemble models], "auto" [all models better than MFO or Baseline], c(mdl_ids in glb_models_lst) [Typically top-rated models in auto]
glb_mdl_ensemble <- "%<d-% setdiff(mygetEnsembleAutoMdlIds(), 'CSM.X.rf')" # "auto" # NULL #: default
### bid0_sp
# c("RFE.X.glm"
# #, "RFE.X.bayesglm"
# , "RFE.X.glmnet", "RFE.X.rpart", "RFE.X.gbm", "RFE.X.rf", "RFE.X.svmLinear", "RFE.X.svmLinear2"
# #, "RFE.X.svmPoly", "RFE.X.svmRadial"
# , "RFE.X.earth", "RFE.X.bagEarth", "RFE.X.Interact.glmnet", "RFE.X.YeoJohnson.glmnet", "RFE.X.center.scale.glmnet", "RFE.X.spatialSign.glmnet")
### bid1_sp
# "auto"; err.abs.fit.sum=76.699774; min.RMSE.fit=0.2186429
# "RFE.X.*"; err.abs.fit.sum=; min.RMSE.fit=0.221114
# c("RFE.X.spatialSign.rf", "RFE.X.YeoJohnson.rf", "RFE.X.center.scale.rf", "RFE.X.rf", "RFE.X.avNNet", "RFE.X.bagEarth", "RFE.X.earth", "RFE.X.gbm", "RFE.X.glmnet", "RFE.X.nnet", "RFE.X.svmLinear2", "RFE.X.glm", "RFE.X.svmLinear", "RFE.X.rpart")
### bid1_sp
### no_sp
# c("RFE.X.rpart", "RFE.X.earth", "RFE.X.glmnet", "RFE.X.glm", "RFE.X.bayesglm", "RFE.X.nnet", "RFE.X.avNNet", "RFE.X.Interact.glmnet", "RFE.X.gbm"
# #,"RFE.X.bagEarth" #takes a long time
# )
### no_sp
### bid1_nosp
# c("CSM.X.glm", "CSM.X.bayesglm", "CSM.X.glmnet", "CSM.X.nnet", "CSM.X.avNNet", "CSM.X.rpart", "CSM.X.gbm", "CSM.X.rf", "CSM.X.lda", "CSM.X.lda2", "CSM.X.earth", "CSM.X.bagEarth", "CSM.X.Interact.glmnet") # Accuracy.OOB(Ensemble.repeatedcv.glmnet) = 0.8240223
# Deleted models with repeated cor(CSM.X.gbm.prob)
# c("CSM.X.glm", "CSM.X.bayesglm", "CSM.X.glmnet", "CSM.X.nnet", "CSM.X.avNNet", "CSM.X.rpart", "CSM.X.gbm", "CSM.X.rf", "CSM.X.lda2", "CSM.X.earth", "CSM.X.bagEarth") # Accuracy.OOB(Ensemble.repeatedcv.glmnet) = 0.8240223
# Used step(ntv.glm) to select simplest model
# c("CSM.X.glmnet", "CSM.X.nnet", "CSM.X.avNNet", "CSM.X.rpart", "CSM.X.gbm", "CSM.X.rf", "CSM.X.earth", "CSM.X.bagEarth") # Accuracy.OOB(Ensemble.repeatedcv.glmnet) = 0.8156425
# Deleted models with cor(CSM.X.gbm.prob) between Q3 & Max
# c("CSM.X.glm", "CSM.X.bayesglm", "CSM.X.glmnet", "CSM.X.nnet", "CSM.X.avNNet", "CSM.X.rpart", "CSM.X.gbm", "CSM.X.rf", "CSM.X.lda2") # Accuracy.OOB(Ensemble.repeatedcv.glmnet) = 0.8184358
### bid1_nosp
#paste(grep("CSM.X", names(glb_models_lst), fixed=TRUE, value=TRUE), collapse='", "')
# Only for classifications; for regressions remove "(.*)\\.prob" form the regex
# tmp_fitobs_df <- glb_fitobs_df[, grep(paste0("^", gsub(".", "\\.", glb_rsp_var_out, fixed = TRUE), "CSM\\.X\\.(.*)\\.prob"), names(glb_fitobs_df), value = TRUE)]; cor_mtrx <- cor(tmp_fitobs_df); cor_vctr <- sort(cor_mtrx[row.names(orderBy(~-Overall, varImp(glb_models_lst[["Ensemble.repeatedcv.glmnet"]])$importance))[1], ]); summary(cor_vctr); cor_vctr
#ntv.glm <- glm(reformulate(indep_vars, glb_rsp_var), family = "binomial", data = glb_fitobs_df)
#step.glm <- step(ntv.glm)
glb_sel_mdl_id <- "Ensemble.repeatedcv.glmnet" #select from c(NULL, "All.X.glmnet", "RFE.X.glmnet")
glb_fin_mdl_id <- NULL #select from c(NULL, glb_sel_mdl_id)
glb_dsp_cols <- c("sold", ".grpid", "color", "condition", "cellular", "carrier", "storage", "biddable", "startprice", "sprice.predict.diff")
glb_out_obs <- NULL # "all" for bid._sp # select from c(NULL, "all", "new", "trn")
glb_out_vars_lst <- list()
# glb_id_var will be the first output column, by default
### !_sp
glb_out_vars_lst[["Probability1"]] <- "%<d-% paste0(glb_rsp_var_out, glb_fin_mdl_id, '.prob')"
### !_sp
### bid._sp
# glb_out_vars_lst[[glb_rsp_var_raw]] <- glb_rsp_var_raw
# glb_out_vars_lst[[paste0(head(unlist(strsplit(glb_rsp_var_out, "")), -1), collapse = "")]] <-
# "%<d-% paste0(glb_rsp_var_out, glb_fin_mdl_id)"
### bid._sp
glbOutStackFnames <- NULL #: default
# c("ebayipads_txt_assoc1_out_bid1_stack.csv") # manual stack
# c("ebayipads_finmdl_bid1_out_nnet_1.csv") # universal stack
glb_out_pfx <- "ebayipads_selmdl2_ensCSM_"
glb_save_envir <- FALSE # or TRUE
# Depict process
glb_analytics_pn <- petrinet(name = "glb_analytics_pn",
trans_df = data.frame(id = 1:6,
name = c("data.training.all","data.new",
"model.selected","model.final",
"data.training.all.prediction","data.new.prediction"),
x=c( -5,-5,-15,-25,-25,-35),
y=c( -5, 5, 0, 0, -5, 5)
),
places_df=data.frame(id=1:4,
name=c("bgn","fit.data.training.all","predict.data.new","end"),
x=c( -0, -20, -30, -40),
y=c( 0, 0, 0, 0),
M0=c( 3, 0, 0, 0)
),
arcs_df=data.frame(
begin=c("bgn","bgn","bgn",
"data.training.all","model.selected","fit.data.training.all",
"fit.data.training.all","model.final",
"data.new","predict.data.new",
"data.training.all.prediction","data.new.prediction"),
end =c("data.training.all","data.new","model.selected",
"fit.data.training.all","fit.data.training.all","model.final",
"data.training.all.prediction","predict.data.new",
"predict.data.new","data.new.prediction",
"end","end")
))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())## Loading required package: grid
glb_analytics_avl_objs <- NULL
glb_chunks_df <- myadd_chunk(NULL, "import.data")## label step_major step_minor label_minor bgn end elapsed
## 1 import.data 1 0 0 10.959 NA NAStep 1.0: import data
chunk option: eval=
#glb_chunks_df <- myadd_chunk(NULL, "import.data")
glb_to_sav <- function() {
sav_allobs_df <<- glb_allobs_df
sav_trnobs_df <<- glb_trnobs_df
if (any(grepl("glb_fitobs_df", ls(envir=globalenv()), fixed=TRUE)) &&
!is.null(glb_fitobs_df)) sav_fitobs_df <<- glb_fitobs_df
if (any(grepl("glb_OOBobs_df", ls(envir=globalenv()), fixed=TRUE)) &&
!is.null(glb_OOBobs_df)) sav_OOBobs_df <<- glb_OOBobs_df
if (any(grepl("glb_newobs_df", ls(envir=globalenv()), fixed=TRUE)) &&
!is.null(glb_newobs_df)) {
#print("Attempting to save glb_newobs_df...")
sav_newobs_df <<- glb_newobs_df
}
if (any(grepl("glb_ctgry_df", ls(envir=globalenv()), fixed=TRUE)) &&
!is.null(glb_ctgry_df)) sav_ctgry_df <<- glb_ctgry_df
if (!is.null(glb_models_lst )) sav_models_lst <<- glb_models_lst
if (!is.null(glb_models_df )) sav_models_df <<- glb_models_df
if (any(grepl("glb_feats_df", ls(envir=globalenv()), fixed=TRUE)) &&
!is.null(glb_feats_df)) sav_feats_df <<- glb_feats_df
if (any(grepl("glb_featsimp_df", ls(envir=globalenv()), fixed=TRUE)) &&
!is.null(glb_featsimp_df)) sav_featsimp_df <<- glb_featsimp_df
}
glb_trnobs_df <- myimport_data(url=glb_trnng_url, comment="glb_trnobs_df",
force_header=TRUE)## [1] "Reading file ./data/eBayiPadTrain.csv..."
## [1] "dimensions of data in ./data/eBayiPadTrain.csv: 1,861 rows x 11 cols"
## description
## 1 iPad is in 8.5+ out of 10 cosmetic condition!
## 2 Previously used, please read description. May show signs of use such as scratches to the screen and
## 3
## 4
## 5 Please feel free to buy. All products have been thoroughly inspected, cleaned and tested to be 100%
## 6
## biddable startprice condition cellular carrier color
## 1 0 159.99 Used 0 None Black
## 2 1 0.99 Used 1 Verizon Unknown
## 3 0 199.99 Used 0 None White
## 4 0 235.00 New other (see details) 0 None Unknown
## 5 0 199.99 Seller refurbished Unknown Unknown Unknown
## 6 1 175.00 Used 1 AT&T Space Gray
## storage productline sold UniqueID
## 1 16 iPad 2 0 10001
## 2 16 iPad 2 1 10002
## 3 16 iPad 4 1 10003
## 4 16 iPad mini 2 0 10004
## 5 Unknown Unknown 0 10005
## 6 32 iPad mini 2 1 10006
## description
## 65
## 283 Pristine condition, comes with a case and stylus.
## 948 \211\333\317Used Apple Ipad 16 gig 1st generation in Great working condition and 100% functional.Very little
## 1354
## 1366 Item still in complete working order, minor scratches, normal wear and tear but no damage. screen is
## 1840
## biddable startprice condition cellular carrier color
## 65 0 195.00 Used 0 None Unknown
## 283 1 20.00 Used 0 None Unknown
## 948 0 110.00 Seller refurbished 0 None Black
## 1354 0 300.00 Used 0 None White
## 1366 1 125.00 Used Unknown Unknown Unknown
## 1840 0 249.99 Used 1 Sprint Space Gray
## storage productline sold UniqueID
## 65 16 iPad mini 0 10065
## 283 64 iPad 1 0 10283
## 948 32 iPad 1 0 10948
## 1354 16 iPad Air 1 11354
## 1366 Unknown iPad 1 1 11366
## 1840 16 iPad Air 1 11840
## description
## 1856 Overall item is in good condition and is fully operational and ready to use. Comes with box and
## 1857 Used. Tested. Guaranteed to work. Physical condition grade B+ does have some light scratches and
## 1858 This item is brand new and was never used; however, the box and/or packaging has been opened.
## 1859
## 1860 This unit has minor scratches on case and several small scratches on the display. \nIt is in
## 1861 30 Day Warranty. Fully functional engraved iPad 1st Generation with signs of normal wear which
## biddable startprice condition cellular carrier
## 1856 0 89.50 Used 1 AT&T
## 1857 0 239.95 Used 0 None
## 1858 0 329.99 New other (see details) 0 None
## 1859 0 400.00 New 0 None
## 1860 0 89.00 Seller refurbished 0 None
## 1861 0 119.99 Used 1 AT&T
## color storage productline sold UniqueID
## 1856 Unknown 16 iPad 1 0 11856
## 1857 Black 32 iPad 4 1 11857
## 1858 Space Gray 16 iPad Air 0 11858
## 1859 Gold 16 iPad mini 3 0 11859
## 1860 Black 64 iPad 1 1 11860
## 1861 Black 64 iPad 1 0 11861
## 'data.frame': 1861 obs. of 11 variables:
## $ description: chr "iPad is in 8.5+ out of 10 cosmetic condition!" "Previously used, please read description. May show signs of use such as scratches to the screen and " "" "" ...
## $ biddable : int 0 1 0 0 0 1 1 0 1 1 ...
## $ startprice : num 159.99 0.99 199.99 235 199.99 ...
## $ condition : chr "Used" "Used" "Used" "New other (see details)" ...
## $ cellular : chr "0" "1" "0" "0" ...
## $ carrier : chr "None" "Verizon" "None" "None" ...
## $ color : chr "Black" "Unknown" "White" "Unknown" ...
## $ storage : chr "16" "16" "16" "16" ...
## $ productline: chr "iPad 2" "iPad 2" "iPad 4" "iPad mini 2" ...
## $ sold : int 0 1 1 0 0 1 1 0 1 1 ...
## $ UniqueID : int 10001 10002 10003 10004 10005 10006 10007 10008 10009 10010 ...
## - attr(*, "comment")= chr "glb_trnobs_df"
## NULL# glb_trnobs_df <- read.delim("data/hygiene.txt", header=TRUE, fill=TRUE, sep="\t",
# fileEncoding='iso-8859-1')
# glb_trnobs_df <- read.table("data/hygiene.dat.labels", col.names=c("dirty"),
# na.strings="[none]")
# glb_trnobs_df$review <- readLines("data/hygiene.dat", n =-1)
# comment(glb_trnobs_df) <- "glb_trnobs_df"
# glb_trnobs_df <- data.frame()
# for (symbol in c("Boeing", "CocaCola", "GE", "IBM", "ProcterGamble")) {
# sym_trnobs_df <-
# myimport_data(url=gsub("IBM", symbol, glb_trnng_url), comment="glb_trnobs_df",
# force_header=TRUE)
# sym_trnobs_df$Symbol <- symbol
# glb_trnobs_df <- myrbind_df(glb_trnobs_df, sym_trnobs_df)
# }
# glb_trnobs_df <-
# glb_trnobs_df %>% dplyr::filter(Year >= 1999)
if (glb_is_separate_newobs_dataset) {
glb_newobs_df <- myimport_data(url=glb_newdt_url, comment="glb_newobs_df",
force_header=TRUE)
# To make plots / stats / checks easier in chunk:inspectORexplore.data
glb_allobs_df <- myrbind_df(glb_trnobs_df, glb_newobs_df);
comment(glb_allobs_df) <- "glb_allobs_df"
} else {
glb_allobs_df <- glb_trnobs_df; comment(glb_allobs_df) <- "glb_allobs_df"
if (!glb_split_entity_newobs_datasets) {
stop("Not implemented yet")
glb_newobs_df <- glb_trnobs_df[sample(1:nrow(glb_trnobs_df),
max(2, nrow(glb_trnobs_df) / 1000)),]
} else if (glb_split_newdata_method == "condition") {
glb_newobs_df <- do.call("subset",
list(glb_trnobs_df, parse(text=glb_split_newdata_condition)))
glb_trnobs_df <- do.call("subset",
list(glb_trnobs_df, parse(text=paste0("!(",
glb_split_newdata_condition,
")"))))
} else if (glb_split_newdata_method == "sample") {
require(caTools)
set.seed(glb_split_sample.seed)
split <- sample.split(glb_trnobs_df[, glb_rsp_var_raw],
SplitRatio=(1-glb_split_newdata_size_ratio))
glb_newobs_df <- glb_trnobs_df[!split, ]
glb_trnobs_df <- glb_trnobs_df[split ,]
} else if (glb_split_newdata_method == "copy") {
glb_trnobs_df <- glb_allobs_df
comment(glb_trnobs_df) <- "glb_trnobs_df"
glb_newobs_df <- glb_allobs_df
comment(glb_newobs_df) <- "glb_newobs_df"
} else stop("glb_split_newdata_method should be %in% c('condition', 'sample', 'copy')")
comment(glb_newobs_df) <- "glb_newobs_df"
myprint_df(glb_newobs_df)
str(glb_newobs_df)
if (glb_split_entity_newobs_datasets) {
myprint_df(glb_trnobs_df)
str(glb_trnobs_df)
}
} ## [1] "Reading file ./data/eBayiPadTest.csv..."
## [1] "dimensions of data in ./data/eBayiPadTest.csv: 798 rows x 10 cols"
## description
## 1 like new
## 2 Item is in great shape. I upgraded to the iPad Air 2 and don't need the mini any longer, even though
## 3 This iPad is working and is tested 100%. It runs great. It is in good condition. Cracked digitizer.
## 4
## 5 Grade A condition means that the Ipad is 100% working condition. Cosmetically 8/9 out of 10 - Will
## 6 Brand new factory sealed iPad in an OPEN BOX...THE BOX ITSELF IS HEAVILY DISTRESSED(see
## biddable startprice condition cellular carrier color
## 1 0 105.00 Used 1 AT&T Unknown
## 2 0 195.00 Used 0 None Unknown
## 3 0 219.99 Used 0 None Unknown
## 4 1 100.00 Used 0 None Unknown
## 5 0 210.99 Manufacturer refurbished 0 None Black
## 6 0 514.95 New other (see details) 0 None Gold
## storage productline UniqueID
## 1 32 iPad 1 11862
## 2 16 iPad mini 2 11863
## 3 64 iPad 3 11864
## 4 16 iPad mini 11865
## 5 32 iPad 3 11866
## 6 64 iPad Air 2 11867
## description
## 1 like new
## 142 iPad mini 1st gen wi-fi 16gb is in perfect working order.
## 309 In excellent condition. Minor scratches on the back. Screen in mint condition. Comes in original
## 312 iPad is in Great condition, the screen is in great condition showing only a few minor scratches, the
## 320 Good condition and fully functional
## 369
## biddable startprice condition cellular carrier color storage
## 1 0 105.00 Used 1 AT&T Unknown 32
## 142 1 0.99 Used 0 None Unknown 16
## 309 0 200.00 Used 1 AT&T Black 32
## 312 1 0.99 Used 0 None Unknown 16
## 320 1 60.00 Used 0 None White 16
## 369 1 197.97 Used 0 None Unknown 64
## productline UniqueID
## 1 iPad 1 11862
## 142 iPad mini 12003
## 309 iPad 3 12170
## 312 iPad mini 2 12173
## 320 iPad 1 12181
## 369 iPad mini 3 12230
## description
## 793 Crack on digitizer near top. Top line of digitizer does not respond to touch. Other than that, all
## 794
## 795
## 796
## 797
## 798 Slightly Used. Includes everything you need plus a nice leather case!\nThere is a slice mark on the
## biddable startprice condition cellular carrier color
## 793 0 104.00 For parts or not working 1 Unknown Black
## 794 0 95.00 Used 1 AT&T Unknown
## 795 1 199.99 Manufacturer refurbished 0 None White
## 796 0 149.99 Used 0 None Unknown
## 797 0 7.99 New Unknown Unknown Unknown
## 798 0 139.00 Used 1 Unknown Black
## storage productline UniqueID
## 793 16 iPad 2 12654
## 794 64 iPad 1 12655
## 795 16 iPad 4 12656
## 796 16 iPad 2 12657
## 797 Unknown iPad 3 12658
## 798 32 Unknown 12659
## 'data.frame': 798 obs. of 10 variables:
## $ description: chr "like new" "Item is in great shape. I upgraded to the iPad Air 2 and don't need the mini any longer, even though " "This iPad is working and is tested 100%. It runs great. It is in good condition. Cracked digitizer." "" ...
## $ biddable : int 0 0 0 1 0 0 0 0 0 1 ...
## $ startprice : num 105 195 220 100 211 ...
## $ condition : chr "Used" "Used" "Used" "Used" ...
## $ cellular : chr "1" "0" "0" "0" ...
## $ carrier : chr "AT&T" "None" "None" "None" ...
## $ color : chr "Unknown" "Unknown" "Unknown" "Unknown" ...
## $ storage : chr "32" "16" "64" "16" ...
## $ productline: chr "iPad 1" "iPad mini 2" "iPad 3" "iPad mini" ...
## $ UniqueID : int 11862 11863 11864 11865 11866 11867 11868 11869 11870 11871 ...
## - attr(*, "comment")= chr "glb_newobs_df"
## NULLif ((num_nas <- sum(is.na(glb_trnobs_df[, glb_rsp_var_raw]))) > 0)
stop("glb_trnobs_df$", glb_rsp_var_raw, " contains NAs for ", num_nas, " obs")
if (nrow(glb_trnobs_df) == nrow(glb_allobs_df))
warning("glb_trnobs_df same as glb_allobs_df")
if (nrow(glb_newobs_df) == nrow(glb_allobs_df))
warning("glb_newobs_df same as glb_allobs_df")
if (length(glb_drop_vars) > 0) {
warning("dropping vars: ", paste0(glb_drop_vars, collapse=", "))
glb_allobs_df <- glb_allobs_df[, setdiff(names(glb_allobs_df), glb_drop_vars)]
glb_trnobs_df <- glb_trnobs_df[, setdiff(names(glb_trnobs_df), glb_drop_vars)]
glb_newobs_df <- glb_newobs_df[, setdiff(names(glb_newobs_df), glb_drop_vars)]
}
#stop(here"); sav_allobs_df <- glb_allobs_df # glb_allobs_df <- sav_allobs_df
# Combine trnent & newobs into glb_allobs_df for easier manipulation
glb_trnobs_df$.src <- "Train"; glb_newobs_df$.src <- "Test";
glbFeatsExclude <- union(glbFeatsExclude, ".src")
glb_allobs_df <- myrbind_df(glb_trnobs_df, glb_newobs_df)
comment(glb_allobs_df) <- "glb_allobs_df"
# Check for duplicates in glb_id_var
if (length(glb_id_var) == 0) {
warning("using .rownames as identifiers for observations")
glb_allobs_df$.rownames <- rownames(glb_allobs_df)
glb_trnobs_df$.rownames <- rownames(subset(glb_allobs_df, .src == "Train"))
glb_newobs_df$.rownames <- rownames(subset(glb_allobs_df, .src == "Test"))
glb_id_var <- ".rownames"
}
if (sum(duplicated(glb_allobs_df[, glb_id_var, FALSE])) > 0)
stop(glb_id_var, " duplicated in glb_allobs_df")
glbFeatsExclude <- union(glbFeatsExclude, glb_id_var)
glb_allobs_df <- orderBy(reformulate(glb_id_var), glb_allobs_df)
glb_trnobs_df <- glb_newobs_df <- NULL
# For Tableau
write.csv(glb_allobs_df, "data/eBayiPadAll.csv", row.names=FALSE)
#stop(here"); glb_to_sav()
# Make any data corrections here
glb_allobs_df[glb_allobs_df[, glb_id_var] == 10986, "cellular"] <- "1"
glb_allobs_df[glb_allobs_df[, glb_id_var] == 10986, "carrier"] <- "T-Mobile"
# - Merge glb_obs_stack_condition & glbObsDropCondition
# - Derive glb_obs_stack|drop_chk_vars from condition automatically
# - Implement glb_obs_stack_condition & glb_obs_stack_chk_vars options
dsp_partition_stats <- function(obs_df, vars=NULL) {
lcl_vars <- NULL
for (var in c(vars, glb_rsp_var_raw)) {
if ((length(unique(obs_df[, var])) > 5) && is.numeric(obs_df[, var])) {
cut_var <- paste0(var, ".cut.fctr")
obs_df[, cut_var] <- cut(obs_df[, var], 3)
lcl_vars <- union(lcl_vars, cut_var)
} else lcl_vars <- union(lcl_vars, var)
}
print("Partition stats:")
print(mycreate_sqlxtab_df(obs_df, union(lcl_vars, ".src")))
for (var in lcl_vars) {
print(freq_df <- mycreate_sqlxtab_df(obs_df, union(var, ".src")))
print(myplot_hbar(freq_df, ".src", ".n", colorcol_name=var))
}
print(mycreate_sqlxtab_df(obs_df, ".src"))
# if (length(unique(glb_allobs_df[, glb_rsp_var_raw])) > 5) {
# cut_var <- paste0(glb_rsp_var_raw, ".cut.fctr")
# glb_allobs_df[, cut_var] <- cut(glb_allobs_df[, glb_rsp_var_raw], 3)
# glbFeatsExclude <- union(glbFeatsExclude, cut_var)
# glb_obs_stack_chk_vars <- union(cut_var, glb_obs_stack_chk_vars)
# } else glb_obs_stack_chk_vars <- union(glb_rsp_var_raw, glb_obs_stack_chk_vars)
# #glb_obs_stack_chk_vars <- union(glb_obs_stack_chk_vars, ".src")
# print(mycreate_sqlxtab_df(glb_allobs_df, union(var, ".src")))
# print(mycreate_sqlxtab_df(glb_allobs_df, union(glb_obs_stack_chk_vars, ".src")))
# for (var in glb_obs_stack_chk_vars) {
# print(mycreate_sqlxtab_df(glb_allobs_df, union(var, ".src")))
# }
# print(mycreate_sqlxtab_df(glb_allobs_df, ".src"))
}
myget_symbols <- function(txt) {
if (is.null(txt)) return(NULL)
#print(getParseData(parse(text=txt, keep.source=TRUE)))
return(unique(subset(getParseData(parse(text=txt, keep.source=TRUE)),
token == "SYMBOL")$text))
}
# tokens <- unlist(strsplit(gsub("[[:punct:]|[:space:]]", " ", glbObsDropCondition), " "))
# tokens <- tokens[tokens != ""]
# glb_obs_drop_chk_vars <- c("biddable") # or NULL
dsp_partition_stats(obs_df=glb_allobs_df, vars=myget_symbols(glbObsDropCondition))## [1] "Partition stats:"## Loading required package: sqldf
## Loading required package: gsubfn
## Loading required package: proto
## Loading required package: RSQLite
## Loading required package: DBI
## Loading required package: tcltk## UniqueID.cut.fctr productline sold .src .n
## 1 (1.18e+04,1.27e+04] iPad 2 NA Test 154
## 2 (1.18e+04,1.27e+04] iPad mini NA Test 111
## 3 (1e+04,1.09e+04] iPad 2 1 Train 104
## 4 (1.18e+04,1.27e+04] Unknown NA Test 92
## 5 (1.09e+04,1.18e+04] iPad mini 0 Train 91
## 6 (1e+04,1.09e+04] iPad 1 1 Train 91
## 7 (1.18e+04,1.27e+04] iPad 1 NA Test 88
## 8 (1.09e+04,1.18e+04] Unknown 0 Train 84
## 9 (1.09e+04,1.18e+04] iPad 2 0 Train 82
## 10 (1.18e+04,1.27e+04] iPad Air NA Test 74
## 11 (1e+04,1.09e+04] iPad mini 1 Train 74
## 12 (1.18e+04,1.27e+04] iPad 4 NA Test 68
## 13 (1.09e+04,1.18e+04] iPad Air 2 0 Train 65
## 14 (1.18e+04,1.27e+04] iPad Air 2 NA Test 62
## 15 (1e+04,1.09e+04] iPad 3 1 Train 61
## 16 (1.09e+04,1.18e+04] iPad Air 0 Train 58
## 17 (1.18e+04,1.27e+04] iPad mini 2 NA Test 56
## 18 (1.18e+04,1.27e+04] iPad 3 NA Test 55
## 19 (1.09e+04,1.18e+04] iPad mini 1 Train 54
## 20 (1e+04,1.09e+04] iPad 2 0 Train 52
## 21 (1e+04,1.09e+04] iPad Air 1 Train 51
## 22 (1.09e+04,1.18e+04] iPad 1 0 Train 50
## 23 (1e+04,1.09e+04] iPad mini 0 Train 48
## 24 (1.09e+04,1.18e+04] Unknown 1 Train 47
## 25 (1.09e+04,1.18e+04] iPad 4 0 Train 45
## 26 (1e+04,1.09e+04] iPad 1 0 Train 44
## 27 (1e+04,1.09e+04] iPad 4 0 Train 43
## 28 (1.09e+04,1.18e+04] iPad mini 3 0 Train 41
## 29 (1.09e+04,1.18e+04] iPad 2 1 Train 40
## 30 (1e+04,1.09e+04] iPad 4 1 Train 40
## 31 (1e+04,1.09e+04] iPad Air 0 Train 39
## 32 (1.18e+04,1.27e+04] iPad mini 3 NA Test 38
## 33 (1.09e+04,1.18e+04] iPad mini 2 0 Train 36
## 34 (1e+04,1.09e+04] iPad Air 2 1 Train 36
## 35 (1e+04,1.09e+04] iPad 3 0 Train 35
## 36 (1.09e+04,1.18e+04] iPad 3 0 Train 34
## 37 (1.09e+04,1.18e+04] iPad Air 2 1 Train 33
## 38 (1e+04,1.09e+04] Unknown 0 Train 29
## 39 (1e+04,1.09e+04] Unknown 1 Train 29
## 40 (1.09e+04,1.18e+04] iPad 1 1 Train 28
## 41 (1e+04,1.09e+04] iPad Air 2 0 Train 28
## 42 (1e+04,1.09e+04] iPad mini 2 1 Train 28
## 43 (1.09e+04,1.18e+04] iPad Air 1 Train 25
## 44 (1.09e+04,1.18e+04] iPad mini 2 1 Train 20
## 45 (1e+04,1.09e+04] iPad mini 2 0 Train 20
## 46 (1.09e+04,1.18e+04] iPad 4 1 Train 19
## 47 (1.09e+04,1.18e+04] iPad 3 1 Train 17
## 48 (1e+04,1.09e+04] iPad mini 3 0 Train 17
## 49 (1e+04,1.09e+04] iPad mini 3 1 Train 15
## 50 (1.09e+04,1.18e+04] iPad mini 3 1 Train 12
## 51 (1.18e+04,1.27e+04] Unknown 0 Train 9
## 52 (1.18e+04,1.27e+04] iPad 1 0 Train 8
## 53 (1.18e+04,1.27e+04] iPad Air 2 0 Train 7
## 54 (1.18e+04,1.27e+04] Unknown 1 Train 6
## 55 (1.18e+04,1.27e+04] iPad 1 1 Train 6
## 56 (1.18e+04,1.27e+04] iPad mini 0 Train 6
## 57 (1.18e+04,1.27e+04] iPad 2 0 Train 5
## 58 (1.18e+04,1.27e+04] iPad 4 0 Train 5
## 59 (1.18e+04,1.27e+04] iPad 4 1 Train 5
## 60 (1.18e+04,1.27e+04] iPad Air 0 Train 5
## 61 (1.18e+04,1.27e+04] iPad mini 3 0 Train 5
## 62 (1.18e+04,1.27e+04] iPad 3 0 Train 4
## 63 (1.18e+04,1.27e+04] iPad mini 1 Train 4
## 64 (1.09e+04,1.18e+04] iPad mini Retina 0 Train 3
## 65 (1.18e+04,1.27e+04] iPad 2 1 Train 3
## 66 (1.18e+04,1.27e+04] iPad 3 1 Train 2
## 67 (1.18e+04,1.27e+04] iPad Air 1 Train 2
## 68 (1.18e+04,1.27e+04] iPad Air 2 1 Train 2
## 69 (1.18e+04,1.27e+04] iPad mini 2 0 Train 2
## 70 (1e+04,1.09e+04] iPad mini Retina 1 Train 2
## 71 (1.09e+04,1.18e+04] iPad 5 1 Train 1
## 72 (1.09e+04,1.18e+04] iPad mini Retina 1 Train 1
## 73 (1.18e+04,1.27e+04] iPad mini 2 1 Train 1
## 74 (1.18e+04,1.27e+04] iPad mini Retina 1 Train 1
## 75 (1e+04,1.09e+04] iPad mini Retina 0 Train 1
## UniqueID.cut.fctr .src .n
## 1 (1e+04,1.09e+04] Train 887
## 2 (1.09e+04,1.18e+04] Train 886
## 3 (1.18e+04,1.27e+04] Test 798
## 4 (1.18e+04,1.27e+04] Train 88
## productline .src .n
## 1 iPad 2 Train 286
## 2 iPad mini Train 277
## 3 iPad 1 Train 227
## 4 Unknown Train 204
## 5 iPad Air Train 180
## 6 iPad Air 2 Train 171
## 7 iPad 4 Train 157
## 8 iPad 2 Test 154
## 9 iPad 3 Train 153
## 10 iPad mini Test 111
## 11 iPad mini 2 Train 107
## 12 Unknown Test 92
## 13 iPad mini 3 Train 90
## 14 iPad 1 Test 88
## 15 iPad Air Test 74
## 16 iPad 4 Test 68
## 17 iPad Air 2 Test 62
## 18 iPad mini 2 Test 56
## 19 iPad 3 Test 55
## 20 iPad mini 3 Test 38
## 21 iPad mini Retina Train 8
## 22 iPad 5 Train 1
## sold .src .n
## 1 0 Train 1001
## 2 1 Train 860
## 3 NA Test 798
## .src .n
## 1 Train 1861
## 2 Test 798if (!is.null(glbObsDropCondition)) {
print(sprintf("Running glbObsDropCondition filter: %s", glbObsDropCondition))
glb_allobs_df <- do.call("subset",
list(glb_allobs_df, parse(text=paste0("!(", glbObsDropCondition, ")"))))
dsp_partition_stats(obs_df=glb_allobs_df, vars=myget_symbols(glbObsDropCondition))
}## [1] "Running glbObsDropCondition filter: (UniqueID %in% c(NULL\n , 11234 #sold=0; 2 other dups(10306, 11503) are sold=1\n , 11844 #sold=0; 3 other dups(11721, 11738, 11812) are sold=1\n )) | \n (productline %in% c('iPad 5', 'iPad mini Retina'))\n # | (biddable != 0) # bid0_sp\n # | (biddable == 0) # bid1_sp\n "
## [1] "Partition stats:"
## UniqueID.cut.fctr productline sold .src .n
## 1 (1.18e+04,1.27e+04] iPad 2 NA Test 154
## 2 (1.18e+04,1.27e+04] iPad mini NA Test 111
## 3 (1e+04,1.09e+04] iPad 2 1 Train 104
## 4 (1.18e+04,1.27e+04] Unknown NA Test 92
## 5 (1.09e+04,1.18e+04] iPad mini 0 Train 91
## 6 (1e+04,1.09e+04] iPad 1 1 Train 91
## 7 (1.18e+04,1.27e+04] iPad 1 NA Test 88
## 8 (1.09e+04,1.18e+04] Unknown 0 Train 84
## 9 (1.09e+04,1.18e+04] iPad 2 0 Train 82
## 10 (1.18e+04,1.27e+04] iPad Air NA Test 74
## 11 (1e+04,1.09e+04] iPad mini 1 Train 74
## 12 (1.18e+04,1.27e+04] iPad 4 NA Test 68
## 13 (1.09e+04,1.18e+04] iPad Air 2 0 Train 65
## 14 (1.18e+04,1.27e+04] iPad Air 2 NA Test 62
## 15 (1e+04,1.09e+04] iPad 3 1 Train 61
## 16 (1.09e+04,1.18e+04] iPad Air 0 Train 58
## 17 (1.18e+04,1.27e+04] iPad mini 2 NA Test 56
## 18 (1.18e+04,1.27e+04] iPad 3 NA Test 55
## 19 (1.09e+04,1.18e+04] iPad mini 1 Train 54
## 20 (1e+04,1.09e+04] iPad 2 0 Train 52
## 21 (1e+04,1.09e+04] iPad Air 1 Train 51
## 22 (1.09e+04,1.18e+04] iPad 1 0 Train 49
## 23 (1e+04,1.09e+04] iPad mini 0 Train 48
## 24 (1.09e+04,1.18e+04] Unknown 1 Train 47
## 25 (1.09e+04,1.18e+04] iPad 4 0 Train 45
## 26 (1e+04,1.09e+04] iPad 1 0 Train 44
## 27 (1e+04,1.09e+04] iPad 4 0 Train 43
## 28 (1.09e+04,1.18e+04] iPad mini 3 0 Train 41
## 29 (1.09e+04,1.18e+04] iPad 2 1 Train 40
## 30 (1e+04,1.09e+04] iPad 4 1 Train 40
## 31 (1e+04,1.09e+04] iPad Air 0 Train 39
## 32 (1.18e+04,1.27e+04] iPad mini 3 NA Test 38
## 33 (1.09e+04,1.18e+04] iPad mini 2 0 Train 36
## 34 (1e+04,1.09e+04] iPad Air 2 1 Train 36
## 35 (1e+04,1.09e+04] iPad 3 0 Train 35
## 36 (1.09e+04,1.18e+04] iPad 3 0 Train 34
## 37 (1.09e+04,1.18e+04] iPad Air 2 1 Train 33
## 38 (1e+04,1.09e+04] Unknown 0 Train 29
## 39 (1e+04,1.09e+04] Unknown 1 Train 29
## 40 (1.09e+04,1.18e+04] iPad 1 1 Train 28
## 41 (1e+04,1.09e+04] iPad Air 2 0 Train 28
## 42 (1e+04,1.09e+04] iPad mini 2 1 Train 28
## 43 (1.09e+04,1.18e+04] iPad Air 1 Train 25
## 44 (1.09e+04,1.18e+04] iPad mini 2 1 Train 20
## 45 (1e+04,1.09e+04] iPad mini 2 0 Train 20
## 46 (1.09e+04,1.18e+04] iPad 4 1 Train 19
## 47 (1.09e+04,1.18e+04] iPad 3 1 Train 17
## 48 (1e+04,1.09e+04] iPad mini 3 0 Train 17
## 49 (1e+04,1.09e+04] iPad mini 3 1 Train 15
## 50 (1.09e+04,1.18e+04] iPad mini 3 1 Train 12
## 51 (1.18e+04,1.27e+04] Unknown 0 Train 9
## 52 (1.18e+04,1.27e+04] iPad 1 0 Train 7
## 53 (1.18e+04,1.27e+04] iPad Air 2 0 Train 7
## 54 (1.18e+04,1.27e+04] Unknown 1 Train 6
## 55 (1.18e+04,1.27e+04] iPad 1 1 Train 6
## 56 (1.18e+04,1.27e+04] iPad mini 0 Train 6
## 57 (1.18e+04,1.27e+04] iPad 2 0 Train 5
## 58 (1.18e+04,1.27e+04] iPad 4 0 Train 5
## 59 (1.18e+04,1.27e+04] iPad 4 1 Train 5
## 60 (1.18e+04,1.27e+04] iPad Air 0 Train 5
## 61 (1.18e+04,1.27e+04] iPad mini 3 0 Train 5
## 62 (1.18e+04,1.27e+04] iPad 3 0 Train 4
## 63 (1.18e+04,1.27e+04] iPad mini 1 Train 4
## 64 (1.18e+04,1.27e+04] iPad 2 1 Train 3
## 65 (1.18e+04,1.27e+04] iPad 3 1 Train 2
## 66 (1.18e+04,1.27e+04] iPad Air 1 Train 2
## 67 (1.18e+04,1.27e+04] iPad Air 2 1 Train 2
## 68 (1.18e+04,1.27e+04] iPad mini 2 0 Train 2
## 69 (1.18e+04,1.27e+04] iPad mini 2 1 Train 1
## UniqueID.cut.fctr .src .n
## 1 (1e+04,1.09e+04] Train 884
## 2 (1.09e+04,1.18e+04] Train 880
## 3 (1.18e+04,1.27e+04] Test 798
## 4 (1.18e+04,1.27e+04] Train 86
## productline .src .n
## 1 iPad 2 Train 286
## 2 iPad mini Train 277
## 3 iPad 1 Train 225
## 4 Unknown Train 204
## 5 iPad Air Train 180
## 6 iPad Air 2 Train 171
## 7 iPad 4 Train 157
## 8 iPad 2 Test 154
## 9 iPad 3 Train 153
## 10 iPad mini Test 111
## 11 iPad mini 2 Train 107
## 12 Unknown Test 92
## 13 iPad mini 3 Train 90
## 14 iPad 1 Test 88
## 15 iPad Air Test 74
## 16 iPad 4 Test 68
## 17 iPad Air 2 Test 62
## 18 iPad mini 2 Test 56
## 19 iPad 3 Test 55
## 20 iPad mini 3 Test 38
## sold .src .n
## 1 0 Train 995
## 2 1 Train 855
## 3 NA Test 798
## .src .n
## 1 Train 1850
## 2 Test 798# Check for duplicates by all features
require(gdata)## Loading required package: gdata
## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.
##
## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.
##
## Attaching package: 'gdata'
##
## The following object is masked from 'package:stats':
##
## nobs
##
## The following object is masked from 'package:utils':
##
## object.size#print(names(glb_allobs_df))
dup_allobs_df <- glb_allobs_df[duplicated2(subset(glb_allobs_df,
select=-c(UniqueID, sold, .src))), ]
dup_allobs_df <- orderBy(~productline+description+startprice+biddable, dup_allobs_df)
print(sprintf("Found %d duplicates by all features:", nrow(dup_allobs_df)))## [1] "Found 304 duplicates by all features:"myprint_df(dup_allobs_df)## description biddable startprice condition cellular
## 1711 1 0.99 For parts or not working Unknown
## 2608 1 0.99 For parts or not working Unknown
## 293 1 5.00 Used Unknown
## 478 1 5.00 Used Unknown
## 385 0 15.00 Used 0
## 390 0 15.00 Used 0
## carrier color storage productline sold UniqueID .src
## 1711 Unknown Unknown 16 Unknown 1 11711 Train
## 2608 Unknown Unknown 16 Unknown NA 12608 Test
## 293 Unknown White 16 Unknown 1 10293 Train
## 478 Unknown White 16 Unknown 1 10478 Train
## 385 None Black 16 Unknown 0 10385 Train
## 390 None Black 16 Unknown 0 10390 Train
## description biddable startprice condition cellular
## 1956 1 0.99 Used 0
## 828 1 249.97 Manufacturer refurbished 1
## 3 0 199.99 Used 0
## 1649 0 209.00 For parts or not working Unknown
## 2111 1 200.00 Used 0
## 172 0 269.00 Used 0
## carrier color storage productline sold UniqueID .src
## 1956 None Unknown 16 iPad 2 NA 11956 Test
## 828 Unknown Black 64 iPad 2 0 10828 Train
## 3 None White 16 iPad 4 1 10003 Train
## 1649 Unknown Unknown 16 iPad Air 0 11649 Train
## 2111 None Space Gray 64 iPad mini 2 NA 12111 Test
## 172 None Unknown 32 iPad mini 2 0 10172 Train
## description biddable startprice condition cellular carrier color
## 8 0 329.99 New 0 None White
## 660 0 329.99 New 0 None White
## 319 0 345.00 New 0 None Gold
## 1886 0 345.00 New 0 None Gold
## 1363 0 498.88 New 1 Verizon Gold
## 1394 0 498.88 New 1 Verizon Gold
## storage productline sold UniqueID .src
## 8 16 iPad mini 3 0 10008 Train
## 660 16 iPad mini 3 0 10660 Train
## 319 16 iPad mini 3 1 10319 Train
## 1886 16 iPad mini 3 NA 11886 Test
## 1363 16 iPad mini 3 0 11363 Train
## 1394 16 iPad mini 3 0 11394 Train# print(dup_allobs_df[, c(glb_id_var, glb_rsp_var_raw,
# "description", "startprice", "biddable")])
# write.csv(dup_allobs_df[, c("UniqueID"), FALSE], "ebayipads_dups.csv", row.names=FALSE)
dupobs_df <- tidyr::unite(dup_allobs_df, "allfeats", -c(sold, UniqueID, .src), sep="#")
# dupobs_df <- dplyr::group_by(dupobs_df, allfeats)
# dupobs_df <- dupobs_df[, "UniqueID", FALSE]
# dupobs_df <- ungroup(dupobs_df)
#
# dupobs_df$.rownames <- row.names(dupobs_df)
grpobs_df <- data.frame(allfeats=unique(dupobs_df[, "allfeats"]))
grpobs_df$.grpid <- row.names(grpobs_df)
dupobs_df <- merge(dupobs_df, grpobs_df)
# dupobs_tbl <- table(dupobs_df$.grpid)
# print(max(dupobs_tbl))
# print(dupobs_tbl[which.max(dupobs_tbl)])
# print(dupobs_df[dupobs_df$.grpid == names(dupobs_tbl[which.max(dupobs_tbl)]), ])
# print(dupobs_df[dupobs_df$.grpid == 106, ])
# for (grpid in c(9, 17, 31, 36, 53))
# print(dupobs_df[dupobs_df$.grpid == grpid, ])
dupgrps_df <- as.data.frame(table(dupobs_df$.grpid, dupobs_df$sold, useNA="ifany"))
names(dupgrps_df)[c(1,2)] <- c(".grpid", "sold")
dupgrps_df$.grpid <- as.numeric(as.character(dupgrps_df$.grpid))
dupgrps_df <- tidyr::spread(dupgrps_df, sold, Freq)
names(dupgrps_df)[-1] <- paste("sold", names(dupgrps_df)[-1], sep=".")
dupgrps_df$.freq <- sapply(1:nrow(dupgrps_df), function(row) sum(dupgrps_df[row, -1]))
myprint_df(orderBy(~-.freq, dupgrps_df))## .grpid sold.0 sold.1 sold.NA .freq
## 40 40 0 6 3 9
## 106 106 0 4 1 5
## 9 9 0 1 3 4
## 17 17 0 3 1 4
## 36 36 0 3 1 4
## 53 53 0 2 2 4
## .grpid sold.0 sold.1 sold.NA .freq
## 10 10 0 2 0 2
## 42 42 0 1 1 2
## 57 57 1 0 1 2
## 66 66 1 0 1 2
## 91 91 0 1 1 2
## 101 101 0 1 1 2
## .grpid sold.0 sold.1 sold.NA .freq
## 130 130 1 0 1 2
## 131 131 1 1 0 2
## 132 132 0 1 1 2
## 133 133 2 0 0 2
## 134 134 0 1 1 2
## 135 135 2 0 0 2print("sold Conflicts:")## [1] "sold Conflicts:"print(subset(dupgrps_df, (sold.0 > 0) & (sold.1 > 0)))## .grpid sold.0 sold.1 sold.NA .freq
## 4 4 1 1 0 2
## 22 22 1 1 0 2
## 23 23 1 1 0 2
## 74 74 1 1 0 2
## 83 83 1 1 0 2
## 84 84 1 1 0 2
## 95 95 1 1 0 2
## 102 102 1 1 0 2
## 109 109 1 1 0 2
## 111 111 1 1 0 2
## 122 122 1 1 0 2
## 131 131 1 1 0 2#dupobs_df[dupobs_df$.grpid == 4, ]
glb_allobs_df <- merge(glb_allobs_df, dupobs_df[, c(glb_id_var, ".grpid")],
by=glb_id_var, all.x=TRUE)
if (nrow(subset(dupgrps_df, (sold.0 > 0) & (sold.1 > 0) & (sold.0 != sold.1))) > 0)
stop("Duplicate conflicts are resolvable")
#subset(glb_allobs_df, .grpid %in% c(25))
#mydsp_obs(list(productline.contains="iPad 1", storage.contains="16", color.contains="Black", carrier.contains="None", cellular.contains="0", condition.contains="Used", startprice=80), cols=c("productline", "storage", "color", "carrier", "cellular", "condition", "startprice", "sold"))
print("Test & Train Groups:")## [1] "Test & Train Groups:"print(subset(dupgrps_df, (sold.NA > 0)))## .grpid sold.0 sold.1 sold.NA .freq
## 1 1 0 1 1 2
## 5 5 1 0 1 2
## 7 7 0 0 2 2
## 8 8 1 0 1 2
## 9 9 0 1 3 4
## 12 12 0 0 2 2
## 14 14 0 1 1 2
## 15 15 0 0 2 2
## 17 17 0 3 1 4
## 18 18 0 2 1 3
## 19 19 0 2 1 3
## 24 24 0 2 1 3
## 26 26 1 0 1 2
## 28 28 1 0 1 2
## 30 30 0 1 1 2
## 32 32 0 0 2 2
## 33 33 0 1 1 2
## 35 35 0 2 1 3
## 36 36 0 3 1 4
## 37 37 0 0 2 2
## 38 38 0 1 1 2
## 40 40 0 6 3 9
## 41 41 0 0 2 2
## 42 42 0 1 1 2
## 43 43 0 1 1 2
## 44 44 0 2 1 3
## 47 47 0 1 1 2
## 48 48 0 0 2 2
## 49 49 0 1 2 3
## 51 51 0 1 1 2
## 53 53 0 2 2 4
## 54 54 0 1 1 2
## 55 55 1 0 2 3
## 56 56 1 0 1 2
## 57 57 1 0 1 2
## 58 58 0 0 2 2
## 59 59 1 0 1 2
## 60 60 1 0 1 2
## 63 63 0 1 1 2
## 66 66 1 0 1 2
## 67 67 1 0 1 2
## 68 68 0 0 2 2
## 69 69 1 0 1 2
## 73 73 0 1 1 2
## 76 76 0 2 1 3
## 86 86 0 0 2 2
## 87 87 1 0 1 2
## 89 89 1 0 1 2
## 90 90 0 0 2 2
## 91 91 0 1 1 2
## 93 93 0 1 1 2
## 94 94 1 0 1 2
## 99 99 0 1 1 2
## 101 101 0 1 1 2
## 103 103 0 1 1 2
## 104 104 1 0 1 2
## 106 106 0 4 1 5
## 107 107 0 1 1 2
## 108 108 0 1 1 2
## 112 112 1 0 1 2
## 114 114 0 1 1 2
## 115 115 0 1 1 2
## 116 116 1 0 1 2
## 117 117 0 2 1 3
## 118 118 0 1 1 2
## 121 121 1 0 1 2
## 124 124 1 0 1 2
## 128 128 0 1 1 2
## 130 130 1 0 1 2
## 132 132 0 1 1 2
## 134 134 0 1 1 2glbFeatsExclude <- c(".grpid", glbFeatsExclude)
#stop(here"); glb_to_sav(); all.equal(sav_allobs_df, glb_allobs_df); glb_allobs_df <- sav_allobs_df
if (!is.null(glbInpMerge)) {
print("Running glbInpMerge specs...")
obsMrg <- data.frame()
for (fName in glbInpMerge$fnames) {
print(sprintf(" Appending rows from %s...", fName))
obsMrg <- rbind(obsMrg, read.csv(fName))
}
glb_allobs_df <- merge(glb_allobs_df, obsMrg, all.x = TRUE)
}## [1] "Running glbInpMerge specs..."
## [1] " Appending rows from ebayipads_finmdl_bid0_sp_out.csv..."
## [1] " Appending rows from ebayipads_mdlens_bid1_sp_out.csv..."dsp_partition_stats(obs_df = glb_allobs_df,
vars = myget_symbols(glb_obs_repartition_train_condition))## [1] "Partition stats:"
## sold .src .n
## 1 0 Train 995
## 2 1 Train 855
## 3 NA Test 798
## sold .src .n
## 1 0 Train 995
## 2 1 Train 855
## 3 NA Test 798
## .src .n
## 1 Train 1850
## 2 Test 798if (!is.null(glb_obs_repartition_train_condition)) {
print(sprintf("Running glb_obs_repartition_train_condition filter: %s",
glb_obs_repartition_train_condition))
# glb_allobs_df <- mutate(glb_allobs_df, .src=ifelse(!is.na(sold) & (sold == 1),
# "Train", "Test"))
# glb_allobs_df <- mutate_(glb_allobs_df,
# .src=interp(ifelse(eval(parse(text="!is.na(sold) & (sold == 1)")),
# "Train", "Test")))
# glb_allobs_df <- within(glb_allobs_df, {
# .src <- ifelse(eval(parse(text="!is.na(sold) & (sold == 1)")),
# "Train", "Test")
# })
# glb_allobs_df <- within(glb_allobs_df, {
# if(eval(parse(text="!is.na(sold) & (sold == 1)"))) .src <- "Train" else
# .src <- "Test"
# })
# with(glb_allobs_df, {
# src <- ifelse(eval(parse(text="!is.na(sold) & (sold == 1)")),
# "Train", "Test")
# })
# glb_allobs_df$.src <- sapply(1:nrow(glb_allobs_df), function (row_ix) ifelse)
# glb_allobs_df[parse(text=paste0("!(", glbObsDropCondition, ")")), ".src"] <- do.call("subset",
# list(glb_allobs_df, ))
glb_trnobs_df <- do.call("subset", list(glb_allobs_df,
parse(text=paste0(" (", glb_obs_repartition_train_condition, ")"))))
glb_trnobs_df$.src <- "Train"
glb_newobs_df <- do.call("subset", list(glb_allobs_df,
parse(text=paste0("!(", glb_obs_repartition_train_condition, ")"))))
glb_newobs_df$.src <- "Test"
glb_allobs_df <- rbind(glb_trnobs_df, glb_newobs_df)
dsp_partition_stats(obs_df = glb_allobs_df,
vars = myget_symbols(glb_obs_repartition_train_condition))
}
glb_chunks_df <- myadd_chunk(glb_chunks_df, "inspect.data", major.inc=TRUE)## label step_major step_minor label_minor bgn end elapsed
## 1 import.data 1 0 0 10.959 24.129 13.171
## 2 inspect.data 2 0 0 24.130 NA NAStep 2.0: inspect data
#print(str(glb_allobs_df))
#View(glb_allobs_df)
dsp_class_dstrb <- function(var) {
xtab_df <- mycreate_xtab_df(glb_allobs_df, c(".src", var))
rownames(xtab_df) <- xtab_df$.src
xtab_df <- subset(xtab_df, select=-.src)
print(xtab_df)
print(xtab_df / rowSums(xtab_df, na.rm=TRUE))
}
# Performed repeatedly in other chunks
glb_chk_data <- function(featsExclude = glbFeatsExclude,
fctrMaxUniqVals = glbFctrMaxUniqVals) {
# Histogram of predictor in glb_trnobs_df & glb_newobs_df
print(myplot_histogram(glb_allobs_df, glb_rsp_var_raw) + facet_wrap(~ .src))
if (glb_is_classification)
dsp_class_dstrb(var=ifelse(glb_rsp_var %in% names(glb_allobs_df),
glb_rsp_var, glb_rsp_var_raw))
mycheck_problem_data(glb_allobs_df, featsExclude, fctrMaxUniqVals)
}
glb_chk_data()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 798 rows containing non-finite values (stat_bin).## Loading required package: reshape2
## sold.0 sold.1 sold.NA
## Test NA NA 798
## Train 995 855 NA
## sold.0 sold.1 sold.NA
## Test NA NA 1
## Train 0.5378378 0.4621622 NA
## [1] "numeric data missing in : "
## sold
## 798
## [1] "numeric data w/ 0s in : "
## biddable sold
## 1437 995
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1513 0 0 0 0 0
## productline .grpid
## 0 NA# Create new features that help diagnostics
if (!is.null(glb_map_rsp_raw_to_var)) {
glb_allobs_df[, glb_rsp_var] <-
glb_map_rsp_raw_to_var(glb_allobs_df[, glb_rsp_var_raw])
mycheck_map_results(mapd_df=glb_allobs_df,
from_col_name=glb_rsp_var_raw, to_col_name=glb_rsp_var)
if (glb_is_classification) dsp_class_dstrb(glb_rsp_var)
}## sold sold.fctr .n
## 1 0 N 995
## 2 1 Y 855
## 3 NA <NA> 798## Warning: Removed 1 rows containing missing values (position_stack).
## sold.fctr.N sold.fctr.Y sold.fctr.NA
## Test NA NA 798
## Train 995 855 NA
## sold.fctr.N sold.fctr.Y sold.fctr.NA
## Test NA NA 1
## Train 0.5378378 0.4621622 NA# check distribution of all numeric data
dsp_numeric_feats_dstrb <- function(feats_vctr) {
grid.newpage()
pushViewport(viewport(layout = grid.layout(ceiling(length(feats_vctr) / 2.0), 2)))
pltIx <- 1
for (feat in feats_vctr) {
#print(sprintf("feat: %s", feat))
if (glb_is_regression)
gp <- myplot_scatter(df=glb_allobs_df, ycol_name=glb_rsp_var, xcol_name=feat,
smooth=TRUE)
if (glb_is_classification)
#gp <- myplot_box(df=glb_allobs_df, ycol_names=feat, xcol_name=glb_rsp_var)
gp <- myplot_violin(glb_allobs_df, ycol_names = feat, xcol_name = glb_rsp_var)
if (inherits(glb_allobs_df[, feat], "factor"))
gp <- gp + facet_wrap(reformulate(feat))
print(gp + labs(title = feat),
vp = viewport(layout.pos.row = ceiling(pltIx / 2.0),
layout.pos.col = ((pltIx - 1) %% 2) + 1))
pltIx <- pltIx + 1
}
}
# dsp_numeric_feats_dstrb(setdiff(names(glb_allobs_df), union(myfind_chr_cols_df(glb_allobs_df), c(glb_rsp_var_raw, glb_rsp_var)))[2:6]) # dsp_numeric_feats_dstrb(c("startprice", "sprice.root3", "sprice.predict.diff"))
add_new_diag_feats <- function(obs_df, ref_df=glb_allobs_df) {
require(plyr)
set.seed(169)
obs_df <- mutate(obs_df,
# <col_name>.NA=is.na(<col_name>),
# <col_name>.fctr=factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# <col_name>.fctr=relevel(factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# "<ref_val>"),
# <col2_name>.fctr=relevel(factor(ifelse(<col1_name> == <val>, "<oth_val>", "<ref_val>")),
# as.factor(c("R", "<ref_val>")),
# ref="<ref_val>"),
# This doesn't work - use sapply instead
# <col_name>.fctr_num=grep(<col_name>, levels(<col_name>.fctr)),
#
# Date.my=as.Date(strptime(Date, "%m/%d/%y %H:%M")),
# Year=year(Date.my),
# Month=months(Date.my),
# Weekday=weekdays(Date.my)
# <col_name>=<table>[as.character(<col2_name>)],
# <col_name>=as.numeric(<col2_name>),
# <col_name> = trunc(<col2_name> / 100),
.rnorm = rnorm(n=nrow(obs_df))
)
# If levels of a factor are different across obs_df & glb_newobs_df; predict.glm fails
# Transformations not handled by mutate
# obs_df$<col_name>.fctr.num <- sapply(1:nrow(obs_df),
# function(row_ix) grep(obs_df[row_ix, "<col_name>"],
# levels(obs_df[row_ix, "<col_name>.fctr"])))
#print(summary(obs_df))
#print(sapply(names(obs_df), function(col) sum(is.na(obs_df[, col]))))
return(obs_df)
}
glb_allobs_df <- add_new_diag_feats(glb_allobs_df)## Loading required package: plyrrequire(dplyr)## Loading required package: dplyr
##
## Attaching package: 'dplyr'
##
## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
##
## The following objects are masked from 'package:gdata':
##
## combine, first, last
##
## The following objects are masked from 'package:stats':
##
## filter, lag
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union#stop(here"); sav_allobs_df <- glb_allobs_df # glb_allobs_df <- sav_allobs_df
# Merge some <descriptor>
# glb_allobs_df$<descriptor>.my <- glb_allobs_df$<descriptor>
# glb_allobs_df[grepl("\\bAIRPORT\\b", glb_allobs_df$<descriptor>.my),
# "<descriptor>.my"] <- "AIRPORT"
# Check distributions of newly transformed / extracted vars
# Enhancement: remove vars that were displayed ealier
dsp_numeric_feats_dstrb(feats_vctr=setdiff(names(glb_allobs_df),
c(myfind_chr_cols_df(glb_allobs_df), glb_rsp_var_raw, glb_rsp_var,
glbFeatsExclude)))
# Convert factors to dummy variables
# Build splines require(splines); bsBasis <- bs(training$age, df=3)
#pairs(subset(glb_trnobs_df, select=-c(col_symbol)))
# Check for glb_newobs_df & glb_trnobs_df features range mismatches
# Other diagnostics:
# print(subset(glb_trnobs_df, <col1_name> == max(glb_trnobs_df$<col1_name>, na.rm=TRUE) &
# <col2_name> <= mean(glb_trnobs_df$<col1_name>, na.rm=TRUE)))
# print(glb_trnobs_df[which.max(glb_trnobs_df$<col_name>),])
# print(<col_name>_freq_glb_trnobs_df <- mycreate_tbl_df(glb_trnobs_df, "<col_name>"))
# print(which.min(table(glb_trnobs_df$<col_name>)))
# print(which.max(table(glb_trnobs_df$<col_name>)))
# print(which.max(table(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>)[, 2]))
# print(table(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>))
# print(table(is.na(glb_trnobs_df$<col1_name>), glb_trnobs_df$<col2_name>))
# print(table(sign(glb_trnobs_df$<col1_name>), glb_trnobs_df$<col2_name>))
# print(mycreate_xtab_df(glb_trnobs_df, <col1_name>))
# print(mycreate_xtab_df(glb_trnobs_df, c(<col1_name>, <col2_name>)))
# print(<col1_name>_<col2_name>_xtab_glb_trnobs_df <-
# mycreate_xtab_df(glb_trnobs_df, c("<col1_name>", "<col2_name>")))
# <col1_name>_<col2_name>_xtab_glb_trnobs_df[is.na(<col1_name>_<col2_name>_xtab_glb_trnobs_df)] <- 0
# print(<col1_name>_<col2_name>_xtab_glb_trnobs_df <-
# mutate(<col1_name>_<col2_name>_xtab_glb_trnobs_df,
# <col3_name>=(<col1_name> * 1.0) / (<col1_name> + <col2_name>)))
# print(mycreate_sqlxtab_df(glb_allobs_df, c("<col1_name>", "<col2_name>")))
# print(<col2_name>_min_entity_arr <-
# sort(tapply(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>, min, na.rm=TRUE)))
# print(<col1_name>_na_by_<col2_name>_arr <-
# sort(tapply(glb_trnobs_df$<col1_name>.NA, glb_trnobs_df$<col2_name>, mean, na.rm=TRUE)))
# Other plots:
# print(myplot_box(df=glb_trnobs_df, ycol_names="<col1_name>"))
# print(myplot_box(df=glb_trnobs_df, ycol_names="<col1_name>", xcol_name="<col2_name>"))
# print(myplot_line(subset(glb_trnobs_df, Symbol %in% c("CocaCola", "ProcterGamble")),
# "Date.POSIX", "StockPrice", facet_row_colnames="Symbol") +
# geom_vline(xintercept=as.numeric(as.POSIXlt("2003-03-01"))) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1983-01-01")))
# )
# print(myplot_line(subset(glb_trnobs_df, Date.POSIX > as.POSIXct("2004-01-01")),
# "Date.POSIX", "StockPrice") +
# geom_line(aes(color=Symbol)) +
# coord_cartesian(xlim=c(as.POSIXct("1990-01-01"),
# as.POSIXct("2000-01-01"))) +
# coord_cartesian(ylim=c(0, 250)) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1997-09-01"))) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1997-11-01")))
# )
# print(myplot_scatter(glb_allobs_df, "<col1_name>", "<col2_name>", smooth=TRUE))
# print(myplot_scatter(glb_allobs_df, "<col1_name>", "<col2_name>", colorcol_name="<Pred.fctr>") +
# geom_point(data=subset(glb_allobs_df, <condition>),
# mapping=aes(x=<x_var>, y=<y_var>), color="red", shape=4, size=5) +
# geom_vline(xintercept=84))
glb_chunks_df <- myadd_chunk(glb_chunks_df, "scrub.data", major.inc=FALSE)## label step_major step_minor label_minor bgn end elapsed
## 2 inspect.data 2 0 0 24.13 28.73 4.6
## 3 scrub.data 2 1 1 28.73 NA NAStep 2.1: scrub data
mycheck_problem_data(glb_allobs_df, featsExclude = glbFeatsExclude,
fctrMaxUniqVals = glbFctrMaxUniqVals)## [1] "numeric data missing in : "
## sold sold.fctr
## 798 798
## [1] "numeric data w/ 0s in : "
## biddable sold
## 1437 995
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1513 0 0 0 0 0
## productline .grpid
## 0 NAfindOffendingCharacter <- function(x, maxStringLength=256){
print(x)
for (c in 1:maxStringLength){
offendingChar <- substr(x,c,c)
#print(offendingChar) #uncomment if you want the indiv characters printed
#the next character is the offending multibyte Character
}
}
# string_vector <- c("test", "Se\x96ora", "works fine")
# lapply(string_vector, findOffendingCharacter)
# lapply(glb_allobs_df$description[29], findOffendingCharacter)
dsp_hdlxtab <- function(str)
print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains=str), ],
c("Headline.pfx", "Headline", glb_rsp_var)))
#dsp_hdlxtab("(1914)|(1939)")
dsp_catxtab <- function(str)
print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains=str), ],
c("Headline.pfx", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# dsp_catxtab("1914)|(1939)")
# dsp_catxtab("19(14|39|64):")
# dsp_catxtab("19..:")
# Merge some categories
# glb_allobs_df$myCategory <-
# plyr::revalue(glb_allobs_df$myCategory, c(
# "#Business Day#Dealbook" = "Business#Business Day#Dealbook",
# "#Business Day#Small Business" = "Business#Business Day#Small Business",
# "dummy" = "dummy"
# ))
# ctgry_xtab_df <- orderBy(reformulate(c("-", ".n")),
# mycreate_sqlxtab_df(glb_allobs_df,
# c("myCategory", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# myprint_df(ctgry_xtab_df)
# write.table(ctgry_xtab_df, paste0(glb_out_pfx, "ctgry_xtab.csv"),
# row.names=FALSE)
# ctgry_cast_df <- orderBy(~ -Y -NA, dcast(ctgry_xtab_df,
# myCategory + NewsDesk + SectionName + SubsectionName ~
# Popular.fctr, sum, value.var=".n"))
# myprint_df(ctgry_cast_df)
# write.table(ctgry_cast_df, paste0(glb_out_pfx, "ctgry_cast.csv"),
# row.names=FALSE)
# print(ctgry_sum_tbl <- table(glb_allobs_df$myCategory, glb_allobs_df[, glb_rsp_var],
# useNA="ifany"))
dsp_chisq.test <- function(...) {
sel_df <- glb_allobs_df[sel_obs(...) &
!is.na(glb_allobs_df$Popular), ]
sel_df$.marker <- 1
ref_df <- glb_allobs_df[!is.na(glb_allobs_df$Popular), ]
mrg_df <- merge(ref_df[, c(glb_id_var, "Popular")],
sel_df[, c(glb_id_var, ".marker")], all.x=TRUE)
mrg_df[is.na(mrg_df)] <- 0
print(mrg_tbl <- table(mrg_df$.marker, mrg_df$Popular))
print("Rows:Selected; Cols:Popular")
#print(mrg_tbl)
print(chisq.test(mrg_tbl))
}
# dsp_chisq.test(Headline.contains="[Ee]bola")
# dsp_chisq.test(Snippet.contains="[Ee]bola")
# dsp_chisq.test(Abstract.contains="[Ee]bola")
# print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains="[Ee]bola"), ],
# c(glb_rsp_var, "NewsDesk", "SectionName", "SubsectionName")))
# print(table(glb_allobs_df$NewsDesk, glb_allobs_df$SectionName))
# print(table(glb_allobs_df$SectionName, glb_allobs_df$SubsectionName))
# print(table(glb_allobs_df$NewsDesk, glb_allobs_df$SectionName, glb_allobs_df$SubsectionName))
# glb_allobs_df$myCategory.fctr <- as.factor(glb_allobs_df$myCategory)
print(table(glb_allobs_df$cellular, glb_allobs_df$carrier, useNA="ifany"))##
## AT&T None Other Sprint T-Mobile Unknown Verizon
## 0 0 1586 0 0 0 0 0
## 1 288 0 3 36 28 172 196
## Unknown 4 4 2 0 0 329 0# glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
# (glb_allobs_df$carrier %in% c("AT&T", "Other")),
# c(glb_id_var, glb_rsp_var_raw, "description", "carrier", "cellular")]
glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
(glb_allobs_df$carrier %in% c("AT&T", "Other")),
"cellular"] <- "1"
# glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
# (glb_allobs_df$carrier %in% c("None")),
# c(glb_id_var, glb_rsp_var_raw, "description", "carrier", "cellular")]
glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
(glb_allobs_df$carrier %in% c("None")),
"cellular"] <- "0"
print(table(glb_allobs_df$cellular, glb_allobs_df$carrier, useNA="ifany"))##
## AT&T None Other Sprint T-Mobile Unknown Verizon
## 0 0 1590 0 0 0 0 0
## 1 292 0 5 36 28 172 196
## Unknown 0 0 0 0 0 329 0Step 2.1: scrub data
glb_chunks_df <- myadd_chunk(glb_chunks_df, "transform.data", major.inc=FALSE)## label step_major step_minor label_minor bgn end elapsed
## 3 scrub.data 2 1 1 28.730 29.758 1.028
## 4 transform.data 2 2 2 29.758 NA NA### Mapping dictionary
#sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
if (!is.null(glb_map_vars)) {
for (feat in glb_map_vars) {
map_df <- myimport_data(url=glb_map_urls[[feat]],
comment="map_df",
print_diagn=TRUE)
glb_allobs_df <- mymap_codes(glb_allobs_df, feat, names(map_df)[2],
map_df, map_join_col_name=names(map_df)[1],
map_tgt_col_name=names(map_df)[2])
}
glbFeatsExclude <- union(glbFeatsExclude, glb_map_vars)
}
### Forced Assignments
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (feat in glb_assign_vars) {
new_feat <- paste0(feat, ".my")
print(sprintf("Forced Assignments for: %s -> %s...", feat, new_feat))
glb_allobs_df[, new_feat] <- glb_allobs_df[, feat]
pairs <- glb_assign_pairs_lst[[feat]]
for (pair_ix in 1:length(pairs$from)) {
if (is.na(pairs$from[pair_ix]))
nobs <- nrow(filter(glb_allobs_df,
is.na(eval(parse(text=feat),
envir=glb_allobs_df)))) else
nobs <- sum(glb_allobs_df[, feat] == pairs$from[pair_ix])
#nobs <- nrow(filter(glb_allobs_df, is.na(Married.fctr))) ; print(nobs)
if ((is.na(pairs$from[pair_ix])) && (is.na(pairs$to[pair_ix])))
stop("what are you trying to do ???")
if (is.na(pairs$from[pair_ix]))
glb_allobs_df[is.na(glb_allobs_df[, feat]), new_feat] <-
pairs$to[pair_ix] else
glb_allobs_df[glb_allobs_df[, feat] == pairs$from[pair_ix], new_feat] <-
pairs$to[pair_ix]
print(sprintf(" %s -> %s for %s obs",
pairs$from[pair_ix], pairs$to[pair_ix], format(nobs, big.mark=",")))
}
glbFeatsExclude <- union(glbFeatsExclude, glb_assign_vars)
}
### Derivations using mapping functions
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (new_feat in glb_derive_vars) {
print(sprintf("Creating new feature: %s...", new_feat))
args_lst <- NULL
for (arg in glbFeatsDerive[[new_feat]]$args)
args_lst[[arg]] <- glb_allobs_df[, arg]
glb_allobs_df[, new_feat] <- do.call(glbFeatsDerive[[new_feat]]$mapfn, args_lst)
}## [1] "Creating new feature: sprice.root2..."
## [1] "Creating new feature: sprice.log10..."
## [1] "Creating new feature: sprice.d20nexp..."
## [1] "Creating new feature: sprice.predict.diff..."
## [1] "Creating new feature: spdiff.cut.fctr..."
## [1] "Creating new feature: descr.my..."
## [1] "Creating new feature: prdl.my.fctr..."#stop(here")
#hex_vctr <- c("\n", "\211", "\235", "\317", "\333")
hex_regex <- paste0(c("\n", "\211", "\235", "\317", "\333"), collapse="|")
for (obs_id in c(10029, 10948, 10136, 10178, 11514, 11904, 12157, 12210, 12659)) {
# tmp_str <- unlist(strsplit(glb_allobs_df[row_pos, "descr.my"], ""))
# glb_allobs_df[row_pos, "descr.my"] <- paste0(tmp_str[!tmp_str %in% hex_vctr],
# collapse="")
row_pos <- which(glb_allobs_df$UniqueID == obs_id)
# glb_allobs_df[row_pos, "descr.my"] <-
# gsub(hex_regex, " ", glb_allobs_df[row_pos, "descr.my"])
}Step 2.2: transform data
#```{r extract_features, cache=FALSE, eval=!is.null(glbFeatsText)}
glb_chunks_df <- myadd_chunk(glb_chunks_df, "extract.features", major.inc=TRUE)## label step_major step_minor label_minor bgn end elapsed
## 4 transform.data 2 2 2 29.758 30.348 0.591
## 5 extract.features 3 0 0 30.349 NA NAextract.features_chunk_df <- myadd_chunk(NULL, "extract.features_bgn")## label step_major step_minor label_minor bgn end
## 1 extract.features_bgn 1 0 0 30.356 NA
## elapsed
## 1 NA# Create new features that help prediction
# <col_name>.lag.2 <- lag(zoo(glb_trnobs_df$<col_name>), -2, na.pad=TRUE)
# glb_trnobs_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
# <col_name>.lag.2 <- lag(zoo(glb_newobs_df$<col_name>), -2, na.pad=TRUE)
# glb_newobs_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
#
# glb_newobs_df[1, "<col_name>.lag.2"] <- glb_trnobs_df[nrow(glb_trnobs_df) - 1,
# "<col_name>"]
# glb_newobs_df[2, "<col_name>.lag.2"] <- glb_trnobs_df[nrow(glb_trnobs_df),
# "<col_name>"]
# glb_allobs_df <- mutate(glb_allobs_df,
# A.P.http=ifelse(grepl("http",Added,fixed=TRUE), 1, 0)
# )
#
# glb_trnobs_df <- mutate(glb_trnobs_df,
# )
#
# glb_newobs_df <- mutate(glb_newobs_df,
# )
# Convert dates to numbers
# typically, dates come in as chars;
# so this must be done before converting chars to factors
#stop(here"); sav_allobs_df <- glb_allobs_df #; glb_allobs_df <- sav_allobs_df
if (!is.null(glb_date_vars)) {
glb_allobs_df <- cbind(glb_allobs_df,
myextract_dates_df(df=glb_allobs_df, vars=glb_date_vars,
id_vars=glb_id_var, rsp_var=glb_rsp_var))
for (sfx in c("", ".POSIX"))
glbFeatsExclude <-
union(glbFeatsExclude,
paste(glb_date_vars, sfx, sep=""))
for (feat in glb_date_vars) {
glb_allobs_df <- orderBy(reformulate(paste0(feat, ".POSIX")), glb_allobs_df)
# print(myplot_scatter(glb_allobs_df, xcol_name=paste0(feat, ".POSIX"),
# ycol_name=glb_rsp_var, colorcol_name=glb_rsp_var))
print(myplot_scatter(glb_allobs_df[glb_allobs_df[, paste0(feat, ".POSIX")] >=
strptime("2012-12-01", "%Y-%m-%d"), ],
xcol_name=paste0(feat, ".POSIX"),
ycol_name=glb_rsp_var, colorcol_name=paste0(feat, ".wkend")))
# Create features that measure the gap between previous timestamp in the data
require(zoo)
z <- zoo(as.numeric(as.POSIXlt(glb_allobs_df[, paste0(feat, ".POSIX")])))
glb_allobs_df[, paste0(feat, ".zoo")] <- z
print(head(glb_allobs_df[, c(glb_id_var, feat, paste0(feat, ".zoo"))]))
print(myplot_scatter(glb_allobs_df[glb_allobs_df[, paste0(feat, ".POSIX")] >
strptime("2012-10-01", "%Y-%m-%d"), ],
xcol_name=paste0(feat, ".zoo"), ycol_name=glb_rsp_var,
colorcol_name=glb_rsp_var))
b <- zoo(, seq(nrow(glb_allobs_df)))
last1 <- as.numeric(merge(z-lag(z, -1), b, all=TRUE)); last1[is.na(last1)] <- 0
glb_allobs_df[, paste0(feat, ".last1.log")] <- log(1 + last1)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last1.log")] > 0, ],
ycol_names=paste0(feat, ".last1.log"),
xcol_name=glb_rsp_var))
last2 <- as.numeric(merge(z-lag(z, -2), b, all=TRUE)); last2[is.na(last2)] <- 0
glb_allobs_df[, paste0(feat, ".last2.log")] <- log(1 + last2)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last2.log")] > 0, ],
ycol_names=paste0(feat, ".last2.log"),
xcol_name=glb_rsp_var))
last10 <- as.numeric(merge(z-lag(z, -10), b, all=TRUE)); last10[is.na(last10)] <- 0
glb_allobs_df[, paste0(feat, ".last10.log")] <- log(1 + last10)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last10.log")] > 0, ],
ycol_names=paste0(feat, ".last10.log"),
xcol_name=glb_rsp_var))
last100 <- as.numeric(merge(z-lag(z, -100), b, all=TRUE)); last100[is.na(last100)] <- 0
glb_allobs_df[, paste0(feat, ".last100.log")] <- log(1 + last100)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last100.log")] > 0, ],
ycol_names=paste0(feat, ".last100.log"),
xcol_name=glb_rsp_var))
glb_allobs_df <- orderBy(reformulate(glb_id_var), glb_allobs_df)
glbFeatsExclude <- union(glbFeatsExclude,
c(paste0(feat, ".zoo")))
# all2$last3 = as.numeric(merge(z-lag(z, -3), b, all = TRUE))
# all2$last5 = as.numeric(merge(z-lag(z, -5), b, all = TRUE))
# all2$last10 = as.numeric(merge(z-lag(z, -10), b, all = TRUE))
# all2$last20 = as.numeric(merge(z-lag(z, -20), b, all = TRUE))
# all2$last50 = as.numeric(merge(z-lag(z, -50), b, all = TRUE))
#
#
# # order table
# all2 = all2[order(all2$id),]
#
# ## fill in NAs
# # count averages
# na.avg = all2 %>% group_by(weekend, hour) %>% dplyr::summarise(
# last1=mean(last1, na.rm=TRUE),
# last3=mean(last3, na.rm=TRUE),
# last5=mean(last5, na.rm=TRUE),
# last10=mean(last10, na.rm=TRUE),
# last20=mean(last20, na.rm=TRUE),
# last50=mean(last50, na.rm=TRUE)
# )
#
# # fill in averages
# na.merge = merge(all2, na.avg, by=c("weekend","hour"))
# na.merge = na.merge[order(na.merge$id),]
# for(i in c("last1", "last3", "last5", "last10", "last20", "last50")) {
# y = paste0(i, ".y")
# idx = is.na(all2[[i]])
# all2[idx,][[i]] <- na.merge[idx,][[y]]
# }
# rm(na.avg, na.merge, b, i, idx, n, pd, sec, sh, y, z)
}
}
rm(last1, last10, last100)## Warning in rm(last1, last10, last100): object 'last1' not found## Warning in rm(last1, last10, last100): object 'last10' not found## Warning in rm(last1, last10, last100): object 'last100' not found# Create factors of string variables
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "factorize.str.vars"), major.inc=TRUE)## label step_major step_minor label_minor
## 1 extract.features_bgn 1 0 0
## 2 extract.features_factorize.str.vars 2 0 0
## bgn end elapsed
## 1 30.356 30.372 0.016
## 2 30.372 NA NA#stop(here"); sav_allobs_df <- glb_allobs_df; #glb_allobs_df <- sav_allobs_df
print(str_vars <- myfind_chr_cols_df(glb_allobs_df))## description condition cellular carrier color
## "description" "condition" "cellular" "carrier" "color"
## storage productline .src .grpid descr.my
## "storage" "productline" ".src" ".grpid" "descr.my"if (length(str_vars <- setdiff(str_vars,
c(glbFeatsExclude, glbFeatsText))) > 0) {
for (var in str_vars) {
warning("Creating factors of string variable: ", var,
": # of unique values: ", length(unique(glb_allobs_df[, var])))
glb_allobs_df[, paste0(var, ".fctr")] <-
relevel(factor(glb_allobs_df[, var]),
names(which.max(table(glb_allobs_df[, var], useNA = "ifany"))))
}
glbFeatsExclude <- union(glbFeatsExclude, str_vars)
}## Warning: Creating factors of string variable: condition: # of unique
## values: 6## Warning: Creating factors of string variable: cellular: # of unique values:
## 3## Warning: Creating factors of string variable: carrier: # of unique values:
## 7## Warning: Creating factors of string variable: color: # of unique values: 5## Warning: Creating factors of string variable: storage: # of unique values:
## 5if (!is.null(glbFeatsText)) {
require(foreach)
require(gsubfn)
require(stringr)
require(tm)
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "process.text"), major.inc=TRUE)
chk_pattern_freq <- function(rex_str, ignore.case=TRUE) {
match_mtrx <- str_extract_all(txt_vctr, regex(rex_str, ignore_case=ignore.case),
simplify=TRUE)
match_df <- as.data.frame(match_mtrx[match_mtrx != ""])
names(match_df) <- "pattern"
return(mycreate_sqlxtab_df(match_df, "pattern"))
}
# match_lst <- gregexpr("\\bok(?!ay)", txt_vctr[746], ignore.case = FALSE, perl=TRUE); print(match_lst)
dsp_pattern <- function(rex_str, ignore.case=TRUE, print.all=TRUE) {
match_lst <- gregexpr(rex_str, txt_vctr, ignore.case = ignore.case, perl=TRUE)
match_lst <- regmatches(txt_vctr, match_lst)
match_df <- data.frame(matches=sapply(match_lst,
function (elems) paste(elems, collapse="#")))
match_df <- subset(match_df, matches != "")
if (print.all)
print(match_df)
return(match_df)
}
dsp_matches <- function(rex_str, ix) {
print(match_pos <- gregexpr(rex_str, txt_vctr[ix], perl=TRUE))
print(str_sub(txt_vctr[ix], (match_pos[[1]] / 100) * 99 + 0,
(match_pos[[1]] / 100) * 100 + 100))
}
myapply_gsub <- function(...) {
if ((length_lst <- length(names(gsub_map_lst))) == 0)
return(txt_vctr)
for (ptn_ix in 1:length_lst) {
if ((ptn_ix %% 10) == 0)
print(sprintf("running gsub for %02d (of %02d): #%s#...", ptn_ix,
length(names(gsub_map_lst)), names(gsub_map_lst)[ptn_ix]))
txt_vctr <- gsub(names(gsub_map_lst)[ptn_ix], gsub_map_lst[[ptn_ix]],
txt_vctr, ...)
}
return(txt_vctr)
}
myapply_txtmap <- function(txt_vctr, ...) {
nrows <- nrow(glb_txt_map_df)
for (ptn_ix in 1:nrows) {
if ((ptn_ix %% 10) == 0)
print(sprintf("running gsub for %02d (of %02d): #%s#...", ptn_ix,
nrows, glb_txt_map_df[ptn_ix, "rex_str"]))
txt_vctr <- gsub(glb_txt_map_df[ptn_ix, "rex_str"],
glb_txt_map_df[ptn_ix, "rpl_str"],
txt_vctr, ...)
}
return(txt_vctr)
#print(txt_vctr <- glb_allobs_df[glb_allobs_df$UniqueID == 11329, "descr.my"])
#strsplit(txt_vctr, "")[[1]][1]
#ptn_ix <- 2; glb_txt_map_df[ptn_ix, ]
#gsub(glb_txt_map_df[ptn_ix, "rex_str"], glb_txt_map_df[ptn_ix, "rpl_str"], txt_vctr)
#print(match_lst <- gregexpr(glb_txt_map_df[ptn_ix, "rex_str"], txt_vctr))
#strsplit(glb_txt_map_df[ptn_ix, "rex_str"], "")[[1]]
}
chk.equal <- function(bgn, end) {
print(all.equal(sav_txt_lst[["Headline"]][bgn:end],
glb_txt_chr_lst[["Headline"]][bgn:end]))
}
dsp.equal <- function(bgn, end) {
print(sav_txt_lst[["Headline"]][bgn:end])
print(glb_txt_chr_lst[["Headline"]][bgn:end])
}
#sav_txt_lst <- glb_txt_chr_lst; all.equal(sav_txt_lst, glb_txt_chr_lst)
#all.equal(sav_txt_lst[["Headline"]][1:4200], glb_txt_chr_lst[["Headline"]][1:4200])
#chk.equal( 1, 100)
#dsp.equal(86, 90)
txt_map_filename <- paste0(glb_txt_munge_filenames_pfx, "map.csv")
if (!file.exists(txt_map_filename))
stop(txt_map_filename, " not found!")
glb_txt_map_df <- read.csv(txt_map_filename, comment.char="#", strip.white=TRUE)
glb_txt_chr_lst <- list();
print(sprintf("Building glb_txt_chr_lst..."))
glb_txt_chr_lst <- foreach(txt_var = glbFeatsText) %dopar% {
# for (txt_var in glbFeatsText) {
txt_vctr <- glb_allobs_df[, txt_var]
names(txt_vctr) <- glb_allobs_df[, glb_id_var]
# myapply_txtmap shd be created as a tm_map::content_transformer ?
#print(glb_txt_map_df)
#txt_var=glbFeatsText[3]; txt_vctr <- glb_txt_chr_lst[[txt_var]]
#print(rex_str <- glb_txt_map_df[3, "rex_str"])
#print(rex_str <- glb_txt_map_df[glb_txt_map_df$rex_str == "\\bWall St\\.", "rex_str"])
#print(rex_str <- glb_txt_map_df[grepl("du Pont", glb_txt_map_df$rex_str), "rex_str"])
#print(rex_str <- glb_txt_map_df[glb_txt_map_df$rpl_str == "versus", "rex_str"])
#print(tmp_vctr <- grep(rex_str, txt_vctr, value=TRUE, ignore.case=FALSE))
#ret_lst <- regexec(rex_str, txt_vctr, ignore.case=FALSE); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
#gsub(rex_str, glb_txt_map_df[glb_txt_map_df$rex_str == rex_str, "rpl_str"], tmp_vctr, ignore.case=FALSE)
#grep("Hong Hong", txt_vctr, value=TRUE)
txt_vctr <- myapply_txtmap(txt_vctr, ignore.case=FALSE)
}
names(glb_txt_chr_lst) <- glbFeatsText
for (txt_var in glbFeatsText) {
print(sprintf("Remaining OK in %s:", txt_var))
txt_vctr <- glb_txt_chr_lst[[txt_var]]
print(chk_pattern_freq(rex_str <- "(?<!(BO|HO|LO))OK(?!(E\\!|ED|IE|IN|S ))",
ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
print(chk_pattern_freq(rex_str <- "Ok(?!(a\\.|ay|in|ra|um))", ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
print(chk_pattern_freq(rex_str <- "(?<!( b| B| c| C| g| G| j| M| p| P| w| W| r| Z|\\(b|ar|bo|Bo|co|Co|Ew|gk|go|ho|ig|jo|kb|ke|Ke|ki|lo|Lo|mo|mt|no|No|po|ra|ro|sm|Sm|Sp|to|To))ok(?!(ay|bo|e |e\\)|e,|e\\.|eb|ed|el|en|er|es|ey|i |ie|in|it|ka|ke|ki|ly|on|oy|ra|st|u |uc|uy|yl|yo))",
ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
}
# txt_vctr <- glb_txt_chr_lst[[glbFeatsText[1]]]
# print(chk_pattern_freq(rex_str <- "(?<!( b| c| C| p|\\(b|bo|co|lo|Lo|Sp|to|To))ok(?!(ay|e |e\\)|e,|e\\.|ed|el|en|es|ey|ie|in|on|ra))", ignore.case=FALSE))
# print(chk_pattern_freq(rex_str <- "ok(?!(ay|el|on|ra))", ignore.case=FALSE))
# dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
# dsp_matches(rex_str, ix=8)
# substr(txt_vctr[86], 5613, 5620)
# substr(glb_allobs_df[301, "review"], 550, 650)
#stop(here"); sav_txt_lst <- glb_txt_chr_lst
for (txt_var in glbFeatsText) {
print(sprintf("Remaining Acronyms in %s:", txt_var))
txt_vctr <- glb_txt_chr_lst[[txt_var]]
print(chk_pattern_freq(rex_str <- "([[:upper:]]\\.( *)){2,}", ignore.case=FALSE))
# Check for names
print(subset(chk_pattern_freq(rex_str <- "(([[:upper:]]+)\\.( *)){1}",
ignore.case=FALSE),
.n > 1))
# dsp_pattern(rex_str="(OK\\.( *)){1}", ignore.case=FALSE)
# dsp_matches(rex_str="(OK\\.( *)){1}", ix=557)
#dsp_matches(rex_str="\\bR\\.I\\.P(\\.*)(\\B)", ix=461)
#dsp_matches(rex_str="\\bR\\.I\\.P(\\.*)", ix=461)
#print(str_sub(txt_vctr[676], 10100, 10200))
#print(str_sub(txt_vctr[74], 1, -1))
}
for (txt_var in glbFeatsText) {
re_str <- "\\b(Fort|Ft\\.|Hong|Las|Los|New|Puerto|Saint|San|St\\.)( |-)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_chr_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl("( |-)[[:upper:]]", pattern))))
print(" consider cleaning if relevant to problem domain; geography name; .n > 1")
#grep("New G", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("St\\. Wins", txt_vctr, value=TRUE, ignore.case=FALSE)
}
#stop(here"); sav_txt_lst <- glb_txt_chr_lst
for (txt_var in glbFeatsText) {
re_str <- "\\b(N|S|E|W|C)( |\\.)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_chr_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl(".", pattern))))
#grep("N Weaver", txt_vctr, value=TRUE, ignore.case=FALSE)
}
for (txt_var in glbFeatsText) {
re_str <- "\\b(North|South|East|West|Central)( |\\.)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_chr_lst[[txt_var]]
if (nrow(filtered_df <- subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl(".", pattern))) > 0)
print(orderBy(~ -.n +pattern, filtered_df))
#grep("Central (African|Bankers|Cast|Italy|Role|Spring)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("East (Africa|Berlin|London|Poland|Rivals|Spring)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("North (American|Korean|West)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("South (Pacific|Street)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("St\\. Martins", txt_vctr, value=TRUE, ignore.case=FALSE)
}
find_cmpnd_wrds <- function(txt_vctr) {
# Enhancements:
# - arg should be txt_corpus instead of txt_vctr
txt_corpus <- Corpus(VectorSource(txt_vctr))
txt_corpus <- tm_map(txt_corpus, content_transformer(tolower), lazy = TRUE)
txt_corpus <- tm_map(txt_corpus, PlainTextDocument, lazy = TRUE)
txt_corpus <- tm_map(txt_corpus, removePunctuation,
preserve_intra_word_dashes = TRUE, lazy = FALSE)
# Defaulting to Tf since TfIdf with normalize = TRUE throws a warning for empty docs
terms_mtrx <- as.matrix(TermDocumentMatrix(txt_corpus,
control = list(weighting = weightTf)))
terms_df <- orderBy(~ -Tf, data.frame(term = dimnames(terms_mtrx)$Terms,
Tf = rowSums(terms_mtrx)))
cmpnd_df <- subset(terms_df, grepl("-", term))
if (nrow(cmpnd_df) == 0) {
print(" No compounded terms found")
return(FALSE)
}
txt_compound_filename <- paste0(glb_txt_munge_filenames_pfx, "compound.csv")
if (!file.exists(txt_compound_filename))
stop(txt_compound_filename, " not found!")
filter_df <- read.csv(txt_compound_filename, comment.char="#", strip.white=TRUE)
cmpnd_df$filter <- FALSE
for (row_ix in 1:nrow(filter_df))
cmpnd_df[!cmpnd_df$filter, "filter"] <-
grepl(filter_df[row_ix, "rex_str"],
cmpnd_df[!cmpnd_df$filter, "term"], ignore.case=TRUE)
cmpnd_df <- subset(cmpnd_df, !filter)
# Bug in tm_map(txt_corpus, removePunctuation, preserve_intra_word_dashes=TRUE) ???
# "net-a-porter" gets converted to "net-aporter"
#grep("net-a-porter", txt_vctr, ignore.case=TRUE, value=TRUE)
#grep("maser-laser", txt_vctr, ignore.case=TRUE, value=TRUE)
#txt_corpus[[which(grepl("net-a-porter", txt_vctr, ignore.case=TRUE))]]
#grep("\\b(across|longer)-(\\w)", cmpnd_df$term, ignore.case=TRUE, value=TRUE)
#grep("(\\w)-(affected|term)\\b", cmpnd_df$term, ignore.case=TRUE, value=TRUE)
print(sprintf("nrow(cmpnd_df): %d", nrow(cmpnd_df)))
myprint_df(cmpnd_df)
}
# This should be run after glb_txt_corpus_lst is created with tolower
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "process.text_reporting_compound_terms"), major.inc=FALSE)
# for (txt_var in glbFeatsText) {
# print(sprintf("Remaining compound terms in %s: ", txt_var))
# find_cmpnd_wrds(txt_vctr = glb_txt_chr_lst[[txt_var]])
# #grep("thirty-five", txt_vctr, ignore.case=TRUE, value=TRUE)
# #rex_str <- glb_txt_map_df[grepl("hirty", glb_txt_map_df$rex_str), "rex_str"]
# }
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "build.corpus"), major.inc=TRUE)
get_txt_terms <- function(terms_TDM) {
terms_mtrx <- as.matrix(as.TermDocumentMatrix(terms_TDM))
docms_mtrx <- as.matrix(as.DocumentTermMatrix(terms_TDM))
terms_df <- data.frame(term = dimnames(terms_mtrx)$Terms,
weight = rowSums(terms_mtrx),
freq = rowSums(terms_mtrx > 0))
terms_df$pos <- 1:nrow(terms_df)
terms_df$cor.y <-
cor(docms_mtrx[glb_allobs_df$.src == "Train",],
as.numeric(glb_allobs_df[glb_allobs_df$.src == "Train", glb_rsp_var]),
use = "pairwise.complete.obs")
terms_df$cor.y.abs <- abs(terms_df$cor.y)
# .rnorm.cor.y.abs <- abs(cor(glb_allobs_df[glb_allobs_df$.src == "Train", ".rnorm"],
# as.numeric(glb_allobs_df[glb_allobs_df$.src == "Train", glb_rsp_var]),
# use = "pairwise.complete.obs"))
terms_df$chisq.stat <- NA; terms_df$chisq.pval <- NA
for (ix in 1:nrow(terms_df)) {
#for (ix in 1:743) {
if (length(unique(docms_mtrx[glb_allobs_df$.src == "Train", ix])) > 1) {
chisq <- chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
glb_allobs_df[glb_allobs_df$.src == "Train", glb_rsp_var])
terms_df[ix, "chisq.stat"] <- chisq$statistic
terms_df[ix, "chisq.pval"] <- chisq$p.value
}
}
nzv_df <- nzv(docms_mtrx[glb_allobs_df$.src == "Train",], freqCut = glb_nzv_freqCut,
uniqueCut = glb_nzv_uniqueCut, saveMetrics = TRUE)
terms_df$nzv.freqRatio <- nzv_df$freqRatio
# terms_df$nzv.freqRatio.cut.fctr <- cut(terms_df$nzv.freqRatio,
# breaks = sort(c(min(terms_df$nzv.freqRatio),
# glb_nzv_freqCut,
# max(terms_df$nzv.freqRatio))))
terms_df$nzv.percentUnique <- nzv_df$percentUnique
# terms_df$nzv.percentUnique.cut.fctr <- cut(terms_df$nzv.percentUnique,
# breaks = sort(c(min(terms_df$nzv.percentUnique) - .Machine$double.neg.eps,
# glb_nzv_uniqueCut,
# max(terms_df$nzv.percentUnique))))
# terms_df$nzv.quad.fctr <- as.factor(paste0("fRatio:", terms_df$nzv.freqRatio.cut.fctr,
# "\n%Unq:", terms_df$nzv.percentUnique.cut.fctr))
terms_df$nzv <- nzv_df$nzv
for (cls in unique(glb_allobs_df[, glb_txt_cor_var])) {
if (!is.na(cls))
terms_df[, paste0("weight.", as.character(cls))] <-
colSums(t(terms_mtrx) *
as.numeric(!is.na(glb_allobs_df[, glb_txt_cor_var]) &
(glb_allobs_df[, glb_txt_cor_var] == cls))) else
terms_df[, paste0("weight.", as.character(cls))] <-
colSums(t(terms_mtrx) *
as.numeric(is.na(glb_allobs_df[, glb_txt_cor_var])))
}
# Check all calls to get_terms_DTM_terms to change returned order assumption
return(terms_df <- orderBy(~ -weight, terms_df))
}
#plt_full_df <- get_terms_DTM_terms(terms_DTM=glb_full_terms_DTM_lst[[txt_var]])
get_corpus_terms <- function(txt_corpus) {
return(terms_df <- get_txt_terms(terms_TDM =
TermDocumentMatrix(txt_corpus, control = glb_txt_terms_control)))
}
myreplacePunctuation <- function(x) {
return(gsub("[[:punct:]]+", " ", gsub("('|-)", "", x)))
}
#stop(here"); glb_to_sav()
glb_txt_corpus_lst <- list()
print(sprintf("Building glb_txt_corpus_lst..."))
glb_txt_corpus_lst <- foreach(txt_var = glbFeatsText, .verbose = FALSE) %dopar% {
#glb_txt_corpus_lst <- foreach(txt_var = glbFeatsText, .verbose = TRUE) %do% {
#for (txt_var in glbFeatsText) {
txt_corpus <- Corpus(VectorSource(glb_txt_chr_lst[[txt_var]]))
txt_corpus <- tm_map(txt_corpus, PlainTextDocument, lazy = TRUE)
txt_corpus <- tm_map(txt_corpus, content_transformer(tolower), lazy = TRUE) #nuppr
# removePunctuation does not replace with whitespace. Use a custom transformer ???
#txt_corpus <- tm_map(txt_corpus, removePunctuation, lazy = TRUE) #npnct<chr_ix>
txt_corpus <- tm_map(txt_corpus, content_transformer(myreplacePunctuation)
, lazy = TRUE) #npnct<chr_ix>
# txt-corpus <- tm_map(txt_corpus, content_transformer(function(x, pattern) gsub(pattern, "", x))
if (!is.null(glb_txt_stop_words[[txt_var]]))
txt_corpus <- tm_map(txt_corpus, removeWords, glb_txt_stop_words[[txt_var]],
lazy = FALSE)#, lazy=TRUE) #nstopwrds
# foreach result is based on .Last.Eval
txt_corpus <- txt_corpus
# glb_txt_corpus_lst[[txt_var]] <- txt_corpus
}
names(glb_txt_corpus_lst) <- glbFeatsText
mycombineSynonyms <- content_transformer(function(x, syn=NULL) {
Reduce(function(a,b) {
gsub(paste0("\\b(", paste(b$syns, collapse = "|"),")\\b"), b$word, a)}, syn, x)
})
#stop(here"); glb_to_sav(); sav_txt_corpus <- glb_txt_corpus_lst[[txt_var]]; all.equal(sav_txt_corpus, glb_txt_corpus_lst[[txt_var]]); glb_txt_corpus_lst[[txt_var]] <- sav_txt_corpus
glb_post_stop_words_terms_df_lst <- list();
glb_post_stop_words_terms_mtrx_lst <- list();
glb_post_stem_words_terms_df_lst <- list();
glb_post_stem_words_terms_mtrx_lst <- list();
for (txt_var in glbFeatsText) {
print(sprintf(" Top_n post-stop term weights for %s:", txt_var))
# This impacts stemming probably due to lazy parameter
print(myprint_df(full_terms_df <-
get_corpus_terms(txt_corpus=glb_txt_corpus_lst[[txt_var]]),
glbFeatsTextTermsMax[[txt_var]]))
glb_post_stop_words_terms_df_lst[[txt_var]] <- full_terms_df
terms_stop_mtrx <- as.matrix(DocumentTermMatrix(glb_txt_corpus_lst[[txt_var]],
control=glb_txt_terms_control))
rownames(terms_stop_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
glb_post_stop_words_terms_mtrx_lst[[txt_var]] <- terms_stop_mtrx
tmp_allobs_df <- glb_allobs_df[, c(glb_id_var, glb_rsp_var)]
tmp_allobs_df$terms.post.stop.n <- rowSums(terms_stop_mtrx > 0)
tmp_allobs_df$terms.post.stop.n.log <- log(1 + tmp_allobs_df$terms.post.stop.n)
tmp_allobs_df$weight.post.stop.sum <- rowSums(terms_stop_mtrx)
print(sprintf(" Top_n stem term weights for %s:", txt_var))
glb_txt_corpus_lst[[txt_var]] <- tm_map(glb_txt_corpus_lst[[txt_var]], stemDocument,
"english", lazy=FALSE)
if (!is.null(glb_txt_synonyms[[txt_var]])) {
syn_lst <- myrmNullObj(glb_txt_synonyms[[txt_var]])
glb_txt_corpus_lst[[txt_var]] <- tm_map(glb_txt_corpus_lst[[txt_var]],
mycombineSynonyms,
syn_lst, lazy=FALSE)
}
print(myprint_df(full_terms_df <- get_corpus_terms(glb_txt_corpus_lst[[txt_var]]),
glbFeatsTextTermsMax[[txt_var]]))
glb_post_stem_words_terms_df_lst[[txt_var]] <- full_terms_df
terms_stem_mtrx <- as.matrix(DocumentTermMatrix(glb_txt_corpus_lst[[txt_var]],
control=glb_txt_terms_control))
rownames(terms_stem_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
glb_post_stem_words_terms_mtrx_lst[[txt_var]] <- terms_stem_mtrx
tmp_allobs_df$terms.post.stem.n <- rowSums(terms_stem_mtrx > 0)
tmp_allobs_df$terms.post.stem.n.log <- log(1 + tmp_allobs_df$terms.post.stem.n)
tmp_allobs_df$weight.post.stem.sum <- rowSums(terms_stem_mtrx)
tmp_allobs_df$terms.n.stem.stop.Ratio <-
1.0 * tmp_allobs_df$terms.post.stem.n / tmp_allobs_df$terms.post.stop.n
tmp_allobs_df[(is.nan(tmp_allobs_df$terms.n.stem.stop.Ratio) |
is.infinite(tmp_allobs_df$terms.n.stem.stop.Ratio)),
"terms.n.stem.stop.Ratio"] <- 1.0
if ((n.errors <- sum(tmp_allobs_df$terms.n.stem.stop.Ratio > 1)) > 0)
stop(n.errors, " obs in tmp_allobs_df have terms.n.stem.stop.Ratio > 1",
" happening due to terms filtered by glb_txt_terms_control$bounds$global[1] but stemmable to other terms")
#print(head(subset(tmp_allobs_df, terms.n.stem.stop.Ratio > 1)))
#glb_allobs_df[(row_ix <- which(glb_allobs_df$UniqueID == 10465)), ]
#terms_stop_mtrx[row_ix, terms_stop_mtrx[row_ix, ] > 0]
#setdiff(names(terms_stem_mtrx[row_ix, terms_stem_mtrx[row_ix, ] > 0]), names(terms_stop_mtrx[row_ix, terms_stop_mtrx[row_ix, ] > 0]))
#mydsp_obs(list(descr.my.contains="updat"))
tmp_allobs_df$weight.sum.stem.stop.Ratio <-
1.0 * tmp_allobs_df$weight.post.stem.sum / tmp_allobs_df$weight.post.stop.sum
tmp_allobs_df[is.nan(tmp_allobs_df$weight.sum.stem.stop.Ratio) |
is.infinite(tmp_allobs_df$weight.sum.stem.stop.Ratio),
"weight.sum.stem.stop.Ratio"] <- 1.0
tmp_trnobs_df <- tmp_allobs_df[!is.na(tmp_allobs_df[, glb_rsp_var]), ]
print(cor(as.matrix(tmp_trnobs_df[, -c(1, 2)]),
as.numeric(tmp_trnobs_df[, glb_rsp_var])))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
tmp_allobs_df <- tmp_allobs_df[, -c(1, 2)]
names(tmp_allobs_df) <- paste(paste0(txt_var_pfx, "."), names(tmp_allobs_df), sep = "")
glb_allobs_df <- cbind(glb_allobs_df, tmp_allobs_df)
glbFeatsExclude <- c(glbFeatsExclude,
paste(paste0(txt_var_pfx, ".terms.post."), c("stop.n", "stem.n"), sep = ""))
}
require(wordcloud)
for (txt_var in glbFeatsText) {
print(sprintf(" Wordcloud post-stem term weights for %s:", txt_var))
m <- glb_post_stem_words_terms_mtrx_lst[[txt_var]]
# calculate the frequency of words
v <- sort(colSums(m), decreasing = TRUE)
myNames <- names(v)
d <- data.frame(word = myNames, freq = v)
wordcloud(d$word, d$freq, min.freq = glb_txt_terms_control$bounds$global[1])
}
for (txt_var in glbFeatsText) {
.rnorm.cor.y.abs <- abs(cor(glb_allobs_df[glb_allobs_df$.src == "Train", ".rnorm"],
as.numeric(glb_allobs_df[glb_allobs_df$.src == "Train", glb_rsp_var]),
use = "pairwise.complete.obs"))
plt_df <- subset(glb_post_stem_words_terms_df_lst[[txt_var]], !is.na(cor.y))
plt_df$nzv.freqRatio.cut.fctr <- cut(plt_df$nzv.freqRatio,
breaks = sort(c(min(plt_df$nzv.freqRatio),
glb_nzv_freqCut,
max(plt_df$nzv.freqRatio))))
plt_df$nzv.percentUnique.cut.fctr <- cut(plt_df$nzv.percentUnique,
breaks = sort(c(min(plt_df$nzv.percentUnique) - .Machine$double.neg.eps,
glb_nzv_uniqueCut,
max(plt_df$nzv.percentUnique))))
plt_df$nzv.quad.fctr <- as.factor(paste0("fRatio:", plt_df$nzv.freqRatio.cut.fctr,
"\n%Unq:", plt_df$nzv.percentUnique.cut.fctr))
labelCnd <- !plt_df$nzv |
(!is.na(plt_df$chisq.pval) & (plt_df$chisq.pval < 0.05)) &
(!is.na(plt_df$cor.y.abs) & (plt_df$cor.y.abs > .rnorm.cor.y.abs))
plt_df$label <- NA; plt_df[labelCnd, "label"] <- plt_df[labelCnd, "term"]
print(ggplot(plt_df, aes(x = cor.y, y = chisq.stat)) +
geom_point(aes(color = nzv.quad.fctr, size = weight)) +
geom_text(aes(label = label), color = "gray50") +
# geom_vline(xintercept = 0) +
geom_vline(xintercept = c(-1, +1) * .rnorm.cor.y.abs, color = "gray",
linetype = "dashed") +
ggtitle(txt_var))
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "extract.DTM"), major.inc=TRUE)
#stop(here")
glb_full_DTM_lst <- list(); glb_sprs_DTM_lst <- list();
for (txt_var in glbFeatsText) {
print(sprintf("Extracting term weights for %s...", txt_var))
txt_corpus <- glb_txt_corpus_lst[[txt_var]]
full_DTM <- DocumentTermMatrix(txt_corpus,
control=glb_txt_terms_control)
sprs_DTM <- removeSparseTerms(full_DTM,
glb_sprs_thresholds[txt_var])
glb_full_DTM_lst[[txt_var]] <- full_DTM
glb_sprs_DTM_lst[[txt_var]] <- sprs_DTM
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "report.DTM"), major.inc=TRUE)
require(reshape2)
for (txt_var in glbFeatsText) {
print(sprintf("Reporting term weights for %s...", txt_var))
full_DTM <- glb_full_DTM_lst[[txt_var]]
sprs_DTM <- glb_sprs_DTM_lst[[txt_var]]
print(" Full TermMatrix:"); print(full_DTM)
full_terms_df <- get_txt_terms(full_DTM)
# full_terms_df <- full_terms_df[, c(2, 1, 3, 4)]
# col_names <- names(full_terms_df)
# col_names[2:length(col_names)] <-
# paste(col_names[2:length(col_names)], ".full", sep="")
# names(full_terms_df) <- col_names
print(" Sparse TermMatrix:"); print(sprs_DTM)
sprs_terms_df <- get_txt_terms(sprs_DTM)
# sprs_terms_df <- sprs_terms_df[, c(2, 1, 3, 4)]
# col_names <- names(sprs_terms_df)
# col_names[2:length(col_names)] <-
# paste(col_names[2:length(col_names)], ".sprs", sep="")
# names(sprs_terms_df) <- col_names
#intersect(names(full_terms_df), names(sprs_terms_df))
terms_df <- merge(full_terms_df, sprs_terms_df, by = c("term", "weight", "freq",
grep("weight\\.", names(full_terms_df), value = TRUE)),
all.x = TRUE, suffixes = c(".full", ".sprs"))
terms_df$in.sprs <- !is.na(terms_df$pos.sprs)
plt_terms_df <- subset(terms_df,
weight >= min(terms_df$weight[!is.na(terms_df$pos.sprs)], na.rm=TRUE))
plt_terms_df$label <- ""
plt_terms_df[is.na(plt_terms_df$pos.sprs), "label"] <-
plt_terms_df[is.na(plt_terms_df$pos.sprs), "term"]
# glb_important_terms[[txt_var]] <- union(glb_important_terms[[txt_var]],
# plt_terms_df[is.na(plt_terms_df$TfIdf.sprs), "term"])
print(myplot_scatter(plt_terms_df, "freq", "weight",
colorcol_name="in.sprs") +
geom_text(aes(label=label), color="Black", size=3.5))
melt_terms_df <- orderBy(~ -value,
melt(terms_df, id.vars="term", measure.vars = c("weight", "freq")))
print(ggplot(melt_terms_df, aes(value, color=variable)) + stat_ecdf() +
geom_hline(yintercept=glb_sprs_thresholds[txt_var],
linetype = "dotted"))
melt_terms_df <- orderBy(~ -value,
melt(subset(terms_df, in.sprs), id.vars="term",
measure.vars=grep("weight.", names(terms_df), value=TRUE)))
print(myplot_hbar(melt_terms_df, "term", "value", colorcol_name="variable"))
melt_terms_df <- orderBy(~ -value,
melt(subset(terms_df, !in.sprs), id.vars="term",
measure.vars=grep("weight.", names(terms_df), value=TRUE)))
print(myplot_hbar(head(melt_terms_df, glbFeatsTextTermsMax[[txt_var]]), "term", "value",
colorcol_name="variable"))
}
# sav_full_DTM_lst <- glb_full_DTM_lst
# print(identical(sav_glb_txt_corpus_lst, glb_txt_corpus_lst))
# print(all.equal(length(sav_glb_txt_corpus_lst), length(glb_txt_corpus_lst)))
# print(all.equal(names(sav_glb_txt_corpus_lst), names(glb_txt_corpus_lst)))
# print(all.equal(sav_glb_txt_corpus_lst[["Headline"]], glb_txt_corpus_lst[["Headline"]]))
# print(identical(sav_full_DTM_lst, glb_full_DTM_lst))
rm(full_terms_mtrx)
# Create txt features
if ((length(glbFeatsText) > 1) &&
(length(unique(pfxs <- sapply(glbFeatsText,
function(txt) toupper(substr(txt, 1, 1))))) < length(glbFeatsText)))
stop("Prefixes for corpus freq terms not unique: ", pfxs)
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "bind.DTM"),
major.inc=TRUE)
#stop(here"); glb_to_sav(); all.equal(sav_allobs_df, glb_allobs_df); glb_allobs_df <- sav_allobs_df
require(tidyr)
for (txt_var in glbFeatsText) {
print(sprintf("Binding DTM for %s...", txt_var))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
txt_full_X_df <- as.data.frame(as.matrix(glb_full_DTM_lst[[txt_var]]))
terms_full_df <- get_txt_terms(glb_full_DTM_lst[[txt_var]])
# make.names adds a period to R keywords e.g. "in", "function"
colnames(txt_full_X_df) <- paste(txt_var_pfx, ".T.",
make.names(colnames(txt_full_X_df)), sep="")
rownames(txt_full_X_df) <- rownames(glb_allobs_df) # warning otherwise
# plt_full_df <- terms_full_df
# names(plt_full_df)[grepl("weight$", names(plt_full_df))] <- "weight.all"
# # gather(plt_full_df[1:5, ], domain, TfIdf, -matches("!(TfIdf)"))
# # gather(plt_full_df[1:5, grepl("TfIdf", names(plt_full_df))], domain, TfIdf)
# # gather(plt_full_df[1:5, ], domain, TfIdf,
# # -names(plt_full_df)[!grepl("TfIdf", names(plt_full_df))])
# plt_full_df <- gather(plt_full_df, domain, weight,
# -c(term, freq, pos, cor.y, cor.y.abs))
# plt_full_df$label <- NA
# top_val_terms <- orderBy(~-weight, terms_full_df)$term[1:glbFeatsTextTermsMax[[txt_var]]]
# plt_full_df[plt_full_df$term %in% top_val_terms, "label"] <-
# plt_full_df[plt_full_df$term %in% top_val_terms, "term"]
# top_cor_terms <- orderBy(~-cor.y.abs,
# terms_full_df)$term[1:glbFeatsTextTermsMax[[txt_var]]]
# plt_full_df[plt_full_df$term %in% top_cor_terms, "label"] <-
# plt_full_df[plt_full_df$term %in% top_cor_terms, "term"]
# #plt_full_df$type <- "none"
# plt_full_df[plt_full_df$term %in% top_val_terms, "type"] <- "top.weight"
# plt_full_df[plt_full_df$term %in% top_cor_terms, "type"] <- "top.cor"
# plt_full_df[plt_full_df$term %in% intersect(top_val_terms, top_cor_terms), "type"] <-
# "top.both"
# cor.y.rnorm <- cor(glb_allobs_df$.rnorm, as.numeric(glb_allobs_df[, glb_rsp_var]),
# use = "pairwise.complete.obs")
# print(ggplot(plt_full_df, aes(x=weight, y=cor.y)) + facet_wrap(~ domain) +
# geom_point(aes(size=freq), color="grey") +
# geom_jitter() +
# geom_text(aes(label=label, color=type), size=3.5) +
# #geom_hline(yintercept=cor.y.rnorm, color="red") +
# geom_hline(yintercept=c(cor.y.rnorm, -cor.y.rnorm), color="red"))
#stop(here"); glb_to_sav()
if (glbFeatsTextFilter == "sparse") {
txt_X_df <- as.data.frame(as.matrix(glb_sprs_DTM_lst[[txt_var]]))
select_terms <- make.names(colnames(txt_X_df))
# colnames(txt_X_df) <- paste(txt_var_pfx, ".T.",
# make.names(colnames(txt_X_df)), sep="")
# rownames(txt_X_df) <- rownames(glb_allobs_df) # warning otherwise
} else if (glbFeatsTextFilter == "top.val") {
select_terms <- orderBy(~-weight,
terms_full_df)$term[1:glbFeatsTextTermsMax[[txt_var]]]
# txt_X_df <- txt_full_X_df[, subset(terms_full_df, term %in% select_terms)$pos,
# FALSE]
} else if (glbFeatsTextFilter == "top.cor") {
select_terms <- orderBy(~-cor.y.abs,
terms_full_df)$term[1:glbFeatsTextTermsMax[[txt_var]]]
# txt_X_df <- txt_full_X_df[, subset(terms_full_df, term %in% select_terms)$pos,
# FALSE]
} else if (glbFeatsTextFilter == "top.chisq") {
select_terms <- orderBy(~-chisq.stat,
subset(terms_full_df, chisq.pval < 0.05)
)$term[1:glbFeatsTextTermsMax[[txt_var]]]
} else if (glbFeatsTextFilter == "union.top.val.cor") {
select_terms <- union(
orderBy(~-weight , terms_full_df)$term[1:glbFeatsTextTermsMax[[txt_var]]],
orderBy(~-cor.y.abs, terms_full_df)$term[1:glbFeatsTextTermsMax[[txt_var]]])
} else stop(
"glbFeatsTextFilter should be one of c('sparse', 'top.val', 'top.cor', 'union.top.val.cor', 'top.chisq') vs. '",
glbFeatsTextFilter, "'")
assoc_terms_lst <- findAssocs(glb_full_DTM_lst[[txt_var]], select_terms,
glbFeatsTextAssocCor[[txt_var]])
assoc_terms <- c(NULL)
for (term in names(assoc_terms_lst))
if (length(assoc_terms_lst[[term]]) > 0)
assoc_terms <- union(assoc_terms, names(assoc_terms_lst[[term]]))
#stop(here"); glb_to_sav()
txt_X_df <- txt_full_X_df[,
subset(terms_full_df, term %in% c(select_terms, assoc_terms))$pos,
FALSE]
glb_allobs_df <- cbind(glb_allobs_df, txt_X_df) # TfIdf is normalized
#glb_allobs_df <- cbind(glb_allobs_df, log_X_df) # if using non-normalized metrics
}
#identical(chk_entity_df, glb_allobs_df)
#chk_entity_df <- glb_allobs_df
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "bind.DXM"),
major.inc=TRUE)
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
glb_punct_vctr <- c("!", "\"", "#", "\\$", "%", "&", "'",
"\\(|\\)",# "\\(", "\\)",
"\\*", "\\+", ",", "-", "\\.", "/", ":", ";",
"<|>", # "<",
"=",
# ">",
"\\?", "@", "\\[", "\\\\", "\\]", "\\^", "_", "`",
"\\{", "\\|", "\\}", "~")
txt_X_df <- glb_allobs_df[, c(glb_id_var, ".rnorm"), FALSE]
txt_X_df <- foreach(txt_var=glbFeatsText, .combine=cbind) %dopar% {
#for (txt_var in glbFeatsText) {
print(sprintf("Binding DXM for %s...", txt_var))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
txt_full_DTM_mtrx <- as.matrix(glb_full_DTM_lst[[txt_var]])
rownames(txt_full_DTM_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
#print(txt_full_DTM_mtrx[txt_full_DTM_mtrx[, "ebola"] != 0, "ebola"])
# Create <txt_var>.T.<term> for glb_important_terms
for (term in glb_important_terms[[txt_var]])
txt_X_df[, paste0(txt_var_pfx, ".T.", make.names(term))] <-
txt_full_DTM_mtrx[, term]
# Create <txt_var>.wrds.n.log & .wrds.unq.n.log
txt_X_df[, paste0(txt_var_pfx, ".wrds.n.log")] <-
log(1 + mycount_pattern_occ("\\w+", glb_txt_chr_lst[[txt_var]]))
txt_X_df[, paste0(txt_var_pfx, ".wrds.unq.n.log")] <-
log(1 + rowSums(txt_full_DTM_mtrx != 0))
txt_X_df[, paste0(txt_var_pfx, ".weight.sum")] <-
rowSums(txt_full_DTM_mtrx)
txt_X_df[, paste0(txt_var_pfx, ".ratio.weight.sum.wrds.n")] <-
txt_X_df[, paste0(txt_var_pfx, ".weight.sum")] /
(exp(txt_X_df[, paste0(txt_var_pfx, ".wrds.n.log")]) - 1)
txt_X_df[is.nan(txt_X_df[, paste0(txt_var_pfx, ".ratio.weight.sum.wrds.n")]),
paste0(txt_var_pfx, ".ratio.weight.sum.wrds.n")] <- 0
# Create <txt_var>.chrs.n.log
txt_X_df[, paste0(txt_var_pfx, ".chrs.n.log")] <-
log(1 + mycount_pattern_occ(".", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".chrs.uppr.n.log")] <-
log(1 + mycount_pattern_occ("[[:upper:]]", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".dgts.n.log")] <-
log(1 + mycount_pattern_occ("[[:digit:]]", glb_allobs_df[, txt_var]))
# Create <txt_var>.npnct?.log
# would this be faster if it's iterated over each row instead of
# each created column ???
for (punct_ix in 1:length(glb_punct_vctr)) {
# smp0 <- " "
# smp1 <- "! \" # $ % & ' ( ) * + , - . / : ; < = > ? @ [ \ ] ^ _ ` { | } ~"
# smp2 <- paste(smp1, smp1, sep=" ")
# print(sprintf("Testing %s pattern:", glb_punct_vctr[punct_ix]))
# results <- mycount_pattern_occ(glb_punct_vctr[punct_ix], c(smp0, smp1, smp2))
# names(results) <- NULL; print(results)
txt_X_df[,
paste0(txt_var_pfx, ".chrs.pnct", sprintf("%02d", punct_ix), ".n.log")] <-
log(1 + mycount_pattern_occ(glb_punct_vctr[punct_ix],
glb_allobs_df[, txt_var]))
}
# print(head(glb_allobs_df[glb_allobs_df[, "A.npnct23.log"] > 0,
# c("UniqueID", "Popular", "Abstract", "A.npnct23.log")]))
# Create <txt_var>.wrds.stop.n.log & <txt_var>ratio.wrds.stop.n.wrds.n
if (!is.null(glb_txt_stop_words[[txt_var]])) {
stop_words_rex_str <- paste0("\\b(",
paste0(glb_txt_stop_words[[txt_var]], collapse="|"),
")\\b")
txt_X_df[, paste0(txt_var_pfx, ".wrds.stop.n", ".log")] <-
log(1 + mycount_pattern_occ(stop_words_rex_str, glb_txt_chr_lst[[txt_var]]))
txt_X_df[, paste0(txt_var_pfx, ".ratio.wrds.stop.n.wrds.n")] <-
exp(txt_X_df[, paste0(txt_var_pfx, ".wrds.stop.n", ".log")] -
txt_X_df[, paste0(txt_var_pfx, ".wrds.n", ".log")])
}
# Create <txt_var>.P.http
txt_X_df[, paste(txt_var_pfx, ".P.http", sep="")] <-
as.integer(0 + mycount_pattern_occ("http", glb_allobs_df[, txt_var]))
# Create <txt_var>.P.mini & air
txt_X_df[, paste(txt_var_pfx, ".P.mini", sep="")] <-
as.integer(0 + mycount_pattern_occ("mini(?!m)", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df[, paste(txt_var_pfx, ".P.air", sep="")] <-
as.integer(0 + mycount_pattern_occ("(?<![fhp])air", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df[, paste(txt_var_pfx, ".P.black", sep="")] <-
as.integer(0 + mycount_pattern_occ("black", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df[, paste(txt_var_pfx, ".P.white", sep="")] <-
as.integer(0 + mycount_pattern_occ("white", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df[, paste(txt_var_pfx, ".P.gold", sep="")] <-
as.integer(0 + mycount_pattern_occ("gold", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df[, paste(txt_var_pfx, ".P.spacegray", sep="")] <-
as.integer(0 + mycount_pattern_occ("spacegray", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df <- subset(txt_X_df, select=-.rnorm)
txt_X_df <- txt_X_df[, -grep(glb_id_var, names(txt_X_df), fixed=TRUE), FALSE]
#glb_allobs_df <- cbind(glb_allobs_df, txt_X_df)
}
glb_allobs_df <- cbind(glb_allobs_df, txt_X_df)
#myplot_box(glb_allobs_df, "A.sum.TfIdf", glb_rsp_var)
# if (sum(is.na(glb_allobs_df$D.P.http)) > 0)
# stop("Why is this happening ?")
# Generate summaries
# print(summary(glb_allobs_df))
# print(sapply(names(glb_allobs_df), function(col) sum(is.na(glb_allobs_df[, col]))))
# print(summary(glb_trnobs_df))
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(summary(glb_newobs_df))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
glbFeatsExclude <- union(glbFeatsExclude,
glbFeatsText)
rm(log_X_df, txt_X_df)
}## Loading required package: stringr## label step_major step_minor label_minor
## 2 extract.features_factorize.str.vars 2 0 0
## 3 extract.features_process.text 3 0 0
## bgn end elapsed
## 2 30.372 30.463 0.091
## 3 30.464 NA NA
## [1] "Building glb_txt_chr_lst..."
## [1] "running gsub for 10 (of 179): #\\bCentral African Republic\\b#..."
## [1] "running gsub for 20 (of 179): #\\bAlejandro G\\. Iñárritu#..."
## [1] "running gsub for 30 (of 179): #\\bC\\.A\\.A\\.#..."
## [1] "running gsub for 40 (of 179): #\\bCV\\.#..."
## [1] "running gsub for 50 (of 179): #\\bE\\.P\\.A\\.#..."
## [1] "running gsub for 60 (of 179): #\\bG\\.I\\. Joe#..."
## [1] "running gsub for 70 (of 179): #\\bISIS\\.#..."
## [1] "running gsub for 80 (of 179): #\\bJ\\.K\\. Simmons#..."
## [1] "running gsub for 90 (of 179): #\\bM\\. Henri Pol#..."
## [1] "running gsub for 100 (of 179): #\\bN\\.Y\\.S\\.E\\.#..."
## [1] "running gsub for 110 (of 179): #\\bR\\.B\\.S\\.#..."
## [1] "running gsub for 120 (of 179): #\\bSteven A\\. Cohen#..."
## [1] "running gsub for 130 (of 179): #\\bV\\.A\\.#..."
## [1] "running gsub for 140 (of 179): #\\bWall Street#..."
## [1] "running gsub for 150 (of 179): #\\bSaint( |-)((Laurent|Lucia)\\b)+#..."
## [1] "running gsub for 160 (of 179): #\\bSouth( |\\\\.)(America|American|Africa|African|Carolina|Dakota|Korea|Korean|Sudan)\\b#..."
## [1] "running gsub for 170 (of 179): #(\\w)-a-year#..."
## [1] "Remaining OK in descr.my:"
## pattern .n
## 1 OK 6
## [[1]]
## [1] NA
## attr(,"match.length")
## [1] NA
##
## [1] NA
## [[1]]
## [1] NA
## attr(,"match.length")
## [1] NA
##
## [1] NA
## [[1]]
## [1] NA
## attr(,"match.length")
## [1] NA
##
## [1] NA
## [[1]]
## [1] NA
## attr(,"match.length")
## [1] NA
##
## [1] NA
## [[1]]
## [1] NA
## attr(,"match.length")
## [1] NA
##
## [1] NA
## [[1]]
## [1] NA
## attr(,"match.length")
## [1] NA
##
## [1] NA
## [1] pattern .n
## <0 rows> (or 0-length row.names)
## [1] pattern .n
## <0 rows> (or 0-length row.names)
## [1] "Remaining Acronyms in descr.my:"
## [1] pattern .n
## <0 rows> (or 0-length row.names)
## pattern .n
## 1 CONDITION. 6
## 2 ONLY. 5
## 3 GB. 4
## 4 BOX. 2
## 5 CORNER. 2
## 6 ESN. 2
## 7 GOOD. 2
## 8 ICLOUD. 2
## 9 IMEI. 2
## 10 IPADS. 2
## 11 LOCKED. 2
## 12 LOCKS. 2
## 13 ON. 2
## 14 SCRATCHES. 2
## 15 TEARS. 2
## 16 USE. 2
## 17 WIFIONLY. 2
## [1] "Remaining #\\b(Fort|Ft\\.|Hong|Las|Los|New|Puerto|Saint|San|St\\.)( |-)(\\w)+# terms in descr.my: "
## pattern .n
## 1 New Open 3
## 4 New Digitizer 1
## 5 New Opened 1
## 6 New Screen 1
## [1] " consider cleaning if relevant to problem domain; geography name; .n > 1"
## [1] "Remaining #\\b(N|S|E|W|C)( |\\.)(\\w)+# terms in descr.my: "
## pattern .n
## 1 C Stock 3
## 2 W blue 1
## [1] "Remaining #\\b(North|South|East|West|Central)( |\\.)(\\w)+# terms in descr.my: "
## label step_major
## 3 extract.features_process.text 3
## 4 extract.features_process.text_reporting_compound_terms 3
## step_minor label_minor bgn end elapsed
## 3 0 0 30.464 32.027 1.563
## 4 1 1 32.028 NA NA
## label step_major
## 4 extract.features_process.text_reporting_compound_terms 3
## 5 extract.features_build.corpus 4
## step_minor label_minor bgn end elapsed
## 4 1 1 32.028 32.032 0.004
## 5 0 0 32.033 NA NA
## [1] "Building glb_txt_corpus_lst..."
## [1] " Top_n post-stop term weights for descr.my:"## Warning in weighting(x): empty document(s): character(0) character(0)
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## character(0) charact## Warning in cor(docms_mtrx[glb_allobs_df$.src == "Train", ],
## as.numeric(glb_allobs_df[glb_allobs_df$.src == : the standard deviation is
## zero## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## [1] "Rows: 347; Cols: 14"
## term weight freq pos cor.y cor.y.abs chisq.stat
## condition condition 160.57765 486 69 -0.034976682 0.034976682 69.49175
## likenew likenew 123.78010 70 175 -0.043168766 0.043168766 22.07653
## in in 102.43546 432 146 -0.071653919 0.071653919 40.30764
## used used 96.66889 229 327 0.008747051 0.008747051 31.92471
## and and 92.37461 331 21 0.006010357 0.006010357 38.45444
## is is 87.72451 335 162 -0.042931491 0.042931491 26.34374
## chisq.pval nzv.freqRatio nzv.percentUnique nzv weight.N
## condition 2.609830e-06 36.97561 1.351351 TRUE 61.61577
## likenew 1.817939e-01 257.71429 0.972973 TRUE 54.11495
## in 1.981535e-02 33.52174 1.351351 TRUE 46.12932
## used 1.017193e-01 54.54839 1.297297 TRUE 33.84672
## and 7.095553e-02 55.24138 1.513514 TRUE 35.73653
## is 5.541564e-01 54.06667 1.567568 TRUE 35.26992
## weight.Y weight.NA
## condition 42.65757 56.30431
## likenew 19.52427 50.14088
## in 26.82731 29.47884
## used 31.76561 31.05656
## and 31.78712 24.85096
## is 23.45595 28.99864
## term weight freq pos cor.y cor.y.abs chisq.stat
## case case 33.533127 76 54 -0.009225083 0.009225083 16.106811
## protector protector 10.206604 18 253 0.006965022 0.006965022 8.315515
## contact contact 10.006409 15 71 0.035032399 0.035032399 9.951124
## 64gb 64gb 8.260734 14 10 -0.030822325 0.030822325 8.352751
## otterbox otterbox 7.546463 10 221 -0.013289954 0.013289954 4.917015
## look look 1.267528 2 182 -0.030370111 0.030370111 1.720453
## chisq.pval nzv.freqRatio nzv.percentUnique nzv weight.N
## case 0.5850931 198.4444 1.0270270 TRUE 16.019773
## protector 0.5026891 611.6667 0.5405405 TRUE 4.336370
## contact 0.0412591 262.5714 0.2702703 TRUE 2.440087
## 64gb 0.3025184 613.6667 0.4324324 TRUE 4.043603
## otterbox 0.5544999 921.5000 0.3783784 TRUE 3.401559
## look 0.4230663 1848.0000 0.1621622 TRUE 1.267528
## weight.Y weight.NA
## case 12.327119 5.186234
## protector 4.386232 1.484003
## contact 5.843907 1.722415
## 64gb 1.260555 2.956575
## otterbox 1.826941 2.317964
## look 0.000000 0.000000
## term weight freq pos cor.y cor.y.abs
## having having 0.7106680 1 140 NA NA
## shipped shipped 0.7106680 1 284 0.02508765 0.02508765
## thats thats 0.6688640 1 310 -0.02155773 0.02155773
## gentle gentle 0.6317049 1 130 -0.02155773 0.02155773
## opening opening 0.6317049 1 216 0.02508765 0.02508765
## previously previously 0.5984572 1 246 NA NA
## chisq.stat chisq.pval nzv.freqRatio nzv.percentUnique nzv
## having NA NA 0 0.05405405 TRUE
## shipped 5.762908e-03 0.9394877 1849 0.10810811 TRUE
## thats 1.281078e-27 1.0000000 1849 0.10810811 TRUE
## gentle 1.281078e-27 1.0000000 1849 0.10810811 TRUE
## opening 5.762908e-03 0.9394877 1849 0.10810811 TRUE
## previously NA NA 0 0.05405405 TRUE
## weight.N weight.Y weight.NA
## having 0.0000000 0.0000000 0.7106680
## shipped 0.0000000 0.7106680 0.0000000
## thats 0.6688640 0.0000000 0.0000000
## gentle 0.6317049 0.0000000 0.0000000
## opening 0.0000000 0.6317049 0.0000000
## previously 0.0000000 0.0000000 0.5984572
## term weight freq pos cor.y cor.y.abs
## having having 0.7106680 1 140 NA NA
## shipped shipped 0.7106680 1 284 0.02508765 0.02508765
## thats thats 0.6688640 1 310 -0.02155773 0.02155773
## gentle gentle 0.6317049 1 130 -0.02155773 0.02155773
## opening opening 0.6317049 1 216 0.02508765 0.02508765
## previously previously 0.5984572 1 246 NA NA
## chisq.stat chisq.pval nzv.freqRatio nzv.percentUnique nzv
## having NA NA 0 0.05405405 TRUE
## shipped 5.762908e-03 0.9394877 1849 0.10810811 TRUE
## thats 1.281078e-27 1.0000000 1849 0.10810811 TRUE
## gentle 1.281078e-27 1.0000000 1849 0.10810811 TRUE
## opening 5.762908e-03 0.9394877 1849 0.10810811 TRUE
## previously NA NA 0 0.05405405 TRUE
## weight.N weight.Y weight.NA
## having 0.0000000 0.0000000 0.7106680
## shipped 0.0000000 0.7106680 0.0000000
## thats 0.6688640 0.0000000 0.0000000
## gentle 0.6317049 0.0000000 0.0000000
## opening 0.0000000 0.6317049 0.0000000
## previously 0.0000000 0.0000000 0.5984572## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## [1] " Top_n stem term weights for descr.my:"## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## Warning in cor(docms_mtrx[glb_allobs_df$.src == "Train", ],
## as.numeric(glb_allobs_df[glb_allobs_df$.src == : the standard deviation is
## zero## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## [1] "Rows: 226; Cols: 14"
## term weight freq pos cor.y cor.y.abs chisq.stat
## condit condit 160.55353 492 52 -0.033248924 0.033248924 68.76178
## likenew likenew 123.78010 70 117 -0.043168766 0.043168766 22.07653
## use use 111.06226 285 210 0.003025811 0.003025811 45.28121
## in in 102.43546 432 98 -0.071653919 0.071653919 40.30764
## and and 92.37461 331 20 0.006010357 0.006010357 38.45444
## is is 87.72451 335 110 -0.042931491 0.042931491 26.34374
## chisq.pval nzv.freqRatio nzv.percentUnique nzv weight.N
## condit 3.361980e-06 36.82927 1.351351 TRUE 61.51671
## likenew 1.817939e-01 257.71429 0.972973 TRUE 54.11495
## use 1.518411e-02 86.63158 1.513514 TRUE 40.77891
## in 1.981535e-02 33.52174 1.351351 TRUE 46.12932
## and 7.095553e-02 55.24138 1.513514 TRUE 35.73653
## is 5.541564e-01 54.06667 1.567568 TRUE 35.26992
## weight.Y weight.NA
## condit 43.14002 55.89681
## likenew 19.52427 50.14088
## use 35.93377 34.34958
## in 26.82731 29.47884
## and 31.78712 24.85096
## is 23.45595 28.99864
## term weight freq pos cor.y cor.y.abs chisq.stat
## brandnew brandnew 37.154496 30 36 -0.014982381 0.014982381 13.498032
## doesnt doesnt 25.357692 57 67 -0.052798870 0.052798870 20.355151
## never never 11.829767 15 132 -0.003422891 0.003422891 7.655336
## set set 10.427103 13 178 0.035657781 0.035657781 8.724937
## restor restor 9.331353 13 168 -0.005404144 0.005404144 9.811595
## absolut absolut 8.451809 12 12 0.066008565 0.066008565 9.350376
## chisq.pval nzv.freqRatio nzv.percentUnique nzv weight.N
## brandnew 0.56388835 609.0000 0.8648649 TRUE 21.081563
## doesnt 0.06065895 129.4286 0.7027027 TRUE 11.924374
## never 0.36396438 459.7500 0.4324324 TRUE 4.776183
## set 0.18964858 920.0000 0.3783784 TRUE 2.246840
## restor 0.19950403 613.3333 0.4324324 TRUE 4.584716
## absolut 0.15481081 921.0000 0.3783784 TRUE 0.000000
## weight.Y weight.NA
## brandnew 11.651061 4.421872
## doesnt 4.248782 9.184536
## never 3.700799 3.352785
## set 5.534968 2.645295
## restor 3.357971 1.388666
## absolut 6.251752 2.200057
## term weight freq pos cor.y cor.y.abs chisq.stat
## 4g 4g 2.200200 3 9 0.0002965679 0.0002965679 2.023052e+00
## sprint sprint 2.037099 2 191 -0.0215577316 0.0215577316 1.281078e-27
## verizon verizon 2.037099 2 212 -0.0215577316 0.0215577316 1.281078e-27
## tmobil tmobil 1.893062 2 203 0.0094480109 0.0094480109 2.023052e+00
## ipad1 ipad1 1.834814 2 101 0.0067428569 0.0067428569 2.023052e+00
## gold gold 1.137069 1 86 -0.0215577316 0.0215577316 1.281078e-27
## chisq.pval nzv.freqRatio nzv.percentUnique nzv weight.N
## 4g 0.3636637 1848 0.1621622 TRUE 0.6989804
## sprint 1.0000000 1849 0.1081081 TRUE 0.7407634
## verizon 1.0000000 1849 0.1081081 TRUE 0.7407634
## tmobil 0.3636637 1848 0.1621622 TRUE 0.7407634
## ipad1 0.3636637 1848 0.1621622 TRUE 0.7977452
## gold 1.0000000 1849 0.1081081 TRUE 1.1370687
## weight.Y weight.NA
## 4g 0.6116078 0.8896114
## sprint 0.0000000 1.2963359
## verizon 0.0000000 1.2963359
## tmobil 1.1522986 0.0000000
## ipad1 1.0370687 0.0000000
## gold 0.0000000 0.0000000
## term weight freq pos cor.y cor.y.abs chisq.stat
## 4g 4g 2.200200 3 9 0.0002965679 0.0002965679 2.023052e+00
## sprint sprint 2.037099 2 191 -0.0215577316 0.0215577316 1.281078e-27
## verizon verizon 2.037099 2 212 -0.0215577316 0.0215577316 1.281078e-27
## tmobil tmobil 1.893062 2 203 0.0094480109 0.0094480109 2.023052e+00
## ipad1 ipad1 1.834814 2 101 0.0067428569 0.0067428569 2.023052e+00
## gold gold 1.137069 1 86 -0.0215577316 0.0215577316 1.281078e-27
## chisq.pval nzv.freqRatio nzv.percentUnique nzv weight.N
## 4g 0.3636637 1848 0.1621622 TRUE 0.6989804
## sprint 1.0000000 1849 0.1081081 TRUE 0.7407634
## verizon 1.0000000 1849 0.1081081 TRUE 0.7407634
## tmobil 0.3636637 1848 0.1621622 TRUE 0.7407634
## ipad1 0.3636637 1848 0.1621622 TRUE 0.7977452
## gold 1.0000000 1849 0.1081081 TRUE 1.1370687
## weight.Y weight.NA
## 4g 0.6116078 0.8896114
## sprint 0.0000000 1.2963359
## verizon 0.0000000 1.2963359
## tmobil 1.1522986 0.0000000
## ipad1 1.0370687 0.0000000
## gold 0.0000000 0.0000000## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## [,1]
## terms.post.stop.n -0.075731613
## terms.post.stop.n.log -0.062526435
## weight.post.stop.sum -0.045009208
## terms.post.stem.n -0.074990216
## terms.post.stem.n.log -0.062220631
## weight.post.stem.sum -0.047105442
## terms.n.stem.stop.Ratio 0.043562113
## weight.sum.stem.stop.Ratio 0.002381527## Loading required package: wordcloud
## Loading required package: RColorBrewer## [1] " Wordcloud post-stem term weights for descr.my:"## Warning in wordcloud(d$word, d$freq, min.freq = glb_txt_terms_control
## $bounds$global[1]): likenew could not be fit on page. It will not be
## plotted.
## Warning: Removed 195 rows containing missing values (geom_text).## label step_major step_minor label_minor bgn
## 5 extract.features_build.corpus 4 0 0 32.033
## 6 extract.features_extract.DTM 5 0 0 53.456
## end elapsed
## 5 53.455 21.422
## 6 NA NA
## [1] "Extracting term weights for descr.my..."## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## label step_major step_minor label_minor bgn
## 6 extract.features_extract.DTM 5 0 0 53.456
## 7 extract.features_report.DTM 6 0 0 54.772
## end elapsed
## 6 54.771 1.315
## 7 NA NA
## [1] "Reporting term weights for descr.my..."
## [1] " Full TermMatrix:"
## <<DocumentTermMatrix (documents: 2648, terms: 226)>>
## Non-/sparse entries: 11341/587107
## Sparsity : 98%
## Maximal term length: 10
## Weighting : term frequency - inverse document frequency (normalized) (tf-idf)## Warning in cor(docms_mtrx[glb_allobs_df$.src == "Train", ],
## as.numeric(glb_allobs_df[glb_allobs_df$.src == : the standard deviation is
## zero## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## [1] " Sparse TermMatrix:"
## <<DocumentTermMatrix (documents: 2648, terms: 19)>>
## Non-/sparse entries: 4701/45611
## Sparsity : 91%
## Maximal term length: 7
## Weighting : term frequency - inverse document frequency (normalized) (tf-idf)## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in myplot_scatter(plt_terms_df, "freq", "weight", colorcol_name =
## "in.sprs"): converting in.sprs to class:factor
## Warning in rm(full_terms_mtrx): object 'full_terms_mtrx' not found## label step_major step_minor label_minor bgn
## 7 extract.features_report.DTM 6 0 0 54.772
## 8 extract.features_bind.DTM 7 0 0 59.251
## end elapsed
## 7 59.251 4.479
## 8 NA NA## Loading required package: tidyr## [1] "Binding DTM for descr.my..."## Warning in cor(docms_mtrx[glb_allobs_df$.src == "Train", ],
## as.numeric(glb_allobs_df[glb_allobs_df$.src == : the standard deviation is
## zero## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## Warning in chisq.test(docms_mtrx[glb_allobs_df$.src == "Train", ix],
## glb_allobs_df[glb_allobs_df$.src == : Chi-squared approximation may be
## incorrect## label step_major step_minor label_minor bgn
## 8 extract.features_bind.DTM 7 0 0 59.251
## 9 extract.features_bind.DXM 8 0 0 61.951
## end elapsed
## 8 61.943 2.692
## 9 NA NA
## [1] "Binding DXM for descr.my..."## Warning in rm(log_X_df, txt_X_df): object 'log_X_df' not found
# Use model info provided in description
# mydsp_obs(list(description.contains="a[[:digit:]]"), cols=glb_dsp_cols, all=TRUE)
# glb_allobs_df[glb_allobs_df$UniqueID == 12474, "prdline.my"] <- "iPad mini"
# glb_allobs_df[glb_allobs_df$UniqueID == 12474, "color"] <- "Space Gray"
# glb_allobs_df[glb_allobs_df$UniqueID == 12474, "cellular"] <- "0"
# glb_allobs_df[glb_allobs_df$UniqueID == 12474, "carrier"] <- "None"
#
# mydsp_obs(list(description.contains="m(.{4})ll"), cols=glb_dsp_cols, all=TRUE)
# glb_allobs_df[glb_allobs_df$UniqueID == 11360, "color"] <- "Black"
# glb_allobs_df[glb_allobs_df$UniqueID == 11360, "storage"] <- "64"
# glb_allobs_df[glb_allobs_df$UniqueID == 11360, "cellular"] <- "0"
# glb_allobs_df[glb_allobs_df$UniqueID == 11360, "carrier"] <- "None"
#
# glb_allobs_df[glb_allobs_df$UniqueID == 11361, "prdline.my"] <- "iPad Air"
# glb_allobs_df[glb_allobs_df$UniqueID == 11361, "storage"] <- "32"
# glb_allobs_df[glb_allobs_df$UniqueID == 11361, "color"] <- "White"
# glb_allobs_df[glb_allobs_df$UniqueID == 11361, "cellular"] <- "0"
# glb_allobs_df[glb_allobs_df$UniqueID == 11361, "carrier"] <- "None"
# mydsp_obs(list(description.contains="mini(?!m)"), perl=TRUE, cols="D.P.mini", all=TRUE)
# mydsp_obs(list(D.P.mini=1), cols="D.P.mini", all=TRUE)
# mydsp_obs(list(D.P.mini=1, productline="Unknown"), cols="D.P.mini", all=TRUE)
# mydsp_obs(list(description.contains="(?<![fhp])air"), perl=TRUE, all=TRUE)
# mydsp_obs(list(description.contains="air"), perl=FALSE, cols="D.P.air", all=TRUE)
# mydsp_obs(list(D.P.air=1, productline="Unknown"), cols="D.P.air", all=TRUE)
# print(mycreate_sqlxtab_df(glb_allobs_df, c("prdline.my", "productline", "D.P.mini",
# glb_rsp_var)))
# print(glb_allobs_df[(glb_allobs_df$productline == "Unknown") &
# (glb_allobs_df$D.P.mini > 0),
# c(glb_id_var, glb_category_var, glb_dsp_cols, glbFeatsText)])
# glb_allobs_df[(glb_allobs_df$D.P.mini == 1) & (glb_allobs_df$productline == "Unknown"),
# "prdline.my"] <- "iPad mini"
# print(mycreate_sqlxtab_df(glb_allobs_df, c("prdline.my", "productline", "D.P.air",
# glb_rsp_var)))
# print(glb_allobs_df[(glb_allobs_df$productline == "Unknown") &
# (glb_allobs_df$D.P.air > 0),
# c(glb_id_var, glb_category_var, glb_dsp_cols, glbFeatsText)])
# #glb_allobs_df[glb_allobs_df$UniqueID == 11863, "D.P.air"] <- 0
# glb_allobs_df[(glb_allobs_df$D.P.air == 1) & (glb_allobs_df$productline == "Unknown"),
# "prdline.my"] <- "iPad Air"
# print(glb_allobs_df[(glb_allobs_df$UniqueID %in% c(11767, 11811, 12156)),
# c(glb_id_var, "sold",
# "prdline.my", "color", "condition", "cellular", "carrier", "storage"
# #, "descr.my"
# )])
# glb_allobs_df[glb_allobs_df$UniqueID == 11767, "prdline.my"] <- "iPad 2"
# glb_allobs_df[glb_allobs_df$UniqueID == 11767, "storage"] <- "32"
# glb_allobs_df[glb_allobs_df$UniqueID == 11811, "prdline.my"] <- "iPad 2"
# glb_allobs_df[glb_allobs_df$UniqueID == 12156, "prdline.my"] <- "iPad 1"
# mydsp_obs(list(prdline.my="Unknown"), all=TRUE)
# tmp_allobs_df <- glb_allobs_df[, "prdline.my", FALSE]
# names(tmp_allobs_df) <- "old.prdline.my"
# glb_allobs_df$prdline.my <-
# plyr::revalue(glb_allobs_df$prdline.my, c(
# # "iPad 1" = "iPad",
# # "iPad 2" = "iPad2+",
# "iPad 3" = "iPad 3+",
# "iPad 4" = "iPad 3+",
# "iPad 5" = "iPad 3+",
#
# "iPad Air" = "iPadAir",
# "iPad Air 2" = "iPadAir",
#
# "iPad mini" = "iPadmini",
# "iPad mini 2" = "iPadmini 2+",
# "iPad mini 3" = "iPadmini 2+",
# "iPad mini Retina" = "iPadmini 2+"
# ))
# tmp_allobs_df$prdline.my <- glb_allobs_df[, "prdline.my"]
# print(mycreate_sqlxtab_df(tmp_allobs_df, c("prdline.my", "old.prdline.my")))
# print(mycreate_sqlxtab_df(tmp_allobs_df, c("prdline.my")))
# print(mycreate_sqlxtab_df(subset(glb_allobs_df, color == "Unknown"),
# c("color", "D.P.black", "D.P.gold", "D.P.spacegray", "D.P.white")))
# print(glb_allobs_df[(glb_allobs_df$color == "Unknown") & (glb_allobs_df$D.P.black > 0),
# c(glb_id_var, "color", "D.P.black", "sold", "prdline.my", "condition",
# "cellular", "carrier", "storage", "descr.my")])
# glb_allobs_df[glb_allobs_df$UniqueID == 12137, "color"] <- "Black"
# print(glb_allobs_df[(glb_allobs_df$color == "Unknown") & (glb_allobs_df$D.P.spacegray > 0),
# c(glb_id_var, "color", "D.P.spacegray", "prdline.my", "condition",
# "cellular", "carrier", "storage", "descr.my")])
# glb_allobs_df[glb_allobs_df$UniqueID %in% c(12106), "color"] <- "Space Gray"
# print(glb_allobs_df[(glb_allobs_df$color == "Unknown") & (glb_allobs_df$D.P.white > 0),
# c(glb_id_var, "color", "D.P.white", "prdline.my", "condition",
# "cellular", "carrier", "storage", "descr.my")])
# glb_allobs_df[glb_allobs_df$UniqueID %in% c(10573, 10809, 10925, 11735), "color"] <-
# "White"
glb_allobs_df$carrier.fctr <- as.factor(glb_allobs_df$carrier)
glb_allobs_df$cellular.fctr <- as.factor(glb_allobs_df$cellular)
glb_allobs_df$color.fctr <- as.factor(glb_allobs_df$color)
# glb_allobs_df$prdline.my.fctr <- as.factor(glb_allobs_df$prdline.my)
glb_allobs_df$storage.fctr <- as.factor(glb_allobs_df$storage)
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
# glb_allobs_df %>%
# unite(prdl.descr.my, c(productline, as.numeric(D.chrs.n.log > 0), sep="#"))
# unite_("prdl.descr.my", interp(~c("productline", as.numeric(D.chrs.n.log > 0), sep="#")))
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
# print(myplot_scatter(glb_trnobs_df, "<col1_name>", "<col2_name>", smooth=TRUE))
#stop(here"); glb_to_sav(); glb_allobs_df <- sav_allobs_df
if (!is.null(glbFeatsPrice)) {
for (var in glbFeatsPrice) {
for (digit in 1:(log10(max(glb_allobs_df[, var], na.rm=TRUE)) + 1)) {
glb_allobs_df[, paste0(var, ".dgt", digit, ".is9")] <-
as.numeric(as.integer((as.integer(glb_allobs_df[, var]) %% (10 ^ digit)) /
(10 ^ (digit - 1))) == 9)
# glb_allobs_df[, paste0(var, ".dgt", digit, ".is9.fctr")] <-
# as.factor(as.integer((as.integer(glb_allobs_df[, var]) %% (10 ^ digit)) /
# (10 ^ (digit - 1))) == 9)
}
for (decimal in 1:2) {
glb_allobs_df[, paste0(var, ".dcm", decimal, ".is9")] <-
as.numeric(as.integer(glb_allobs_df[, var] * (10 ^ decimal)) %% 10 == 9)
# glb_allobs_df[, paste0(var, ".dcm", decimal, ".is9.fctr")] <-
# as.factor(as.integer(glb_allobs_df[, var] * (10 ^ decimal)) %% 10 == 9)
}
}
#as.numeric((as.integer(startprice) %% 10) == 9)
}
rm(corpus_lst
, glb_sprs_DTM_lst #, glb_full_DTM_lst
, txt_corpus, txt_vctr)## Warning in rm(corpus_lst, glb_sprs_DTM_lst, txt_corpus, txt_vctr): object
## 'corpus_lst' not foundextract.features_chunk_df <- myadd_chunk(extract.features_chunk_df, "extract.features_end",
major.inc=TRUE)## label step_major step_minor label_minor bgn
## 9 extract.features_bind.DXM 8 0 0 61.951
## 10 extract.features_end 9 0 0 245.628
## end elapsed
## 9 245.627 183.677
## 10 NA NAmyplt_chunk(extract.features_chunk_df)## label step_major
## 9 extract.features_bind.DXM 8
## 5 extract.features_build.corpus 4
## 7 extract.features_report.DTM 6
## 8 extract.features_bind.DTM 7
## 3 extract.features_process.text 3
## 6 extract.features_extract.DTM 5
## 2 extract.features_factorize.str.vars 2
## 1 extract.features_bgn 1
## 4 extract.features_process.text_reporting_compound_terms 3
## step_minor label_minor bgn end elapsed duration
## 9 0 0 61.951 245.627 183.677 183.676
## 5 0 0 32.033 53.455 21.422 21.422
## 7 0 0 54.772 59.251 4.479 4.479
## 8 0 0 59.251 61.943 2.692 2.692
## 3 0 0 30.464 32.027 1.563 1.563
## 6 0 0 53.456 54.771 1.315 1.315
## 2 0 0 30.372 30.463 0.091 0.091
## 1 0 0 30.356 30.372 0.016 0.016
## 4 1 1 32.028 32.032 0.004 0.004
## [1] "Total Elapsed Time: 245.627 secs"
# if (glb_save_envir)
# save(glb_feats_df,
# glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
# file=paste0(glb_out_pfx, "extract_features_dsk.RData"))
# load(paste0(glb_out_pfx, "extract_features_dsk.RData"))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all","data.new")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
#glb_chunks_df <- myadd_chunk(glb_chunks_df, "manage.missing.data", major.inc=TRUE)Step 3.0: extract features
glb_chunks_df <- myadd_chunk(glb_chunks_df, "manage.missing.data", major.inc=FALSE)## label step_major step_minor label_minor bgn end
## 5 extract.features 3 0 0 30.349 250.783
## 6 manage.missing.data 3 1 1 250.784 NA
## elapsed
## 5 220.434
## 6 NA# If mice crashes with error: Error in get(as.character(FUN), mode = "function", envir = envir) : object 'State' of mode 'function' was not found
# consider excluding 'State' as a feature
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
# glb_trnobs_df <- na.omit(glb_trnobs_df)
# glb_newobs_df <- na.omit(glb_newobs_df)
# df[is.na(df)] <- 0
mycheck_problem_data(glb_allobs_df, featsExclude = glbFeatsExclude,
fctrMaxUniqVals = glbFctrMaxUniqVals)## [1] "numeric data missing in : "
## sold sold.fctr
## 798 798
## [1] "numeric data w/ 0s in : "
## biddable sold
## 1437 995
## sprice.log10 cellular.fctr
## 31 1590
## D.terms.post.stop.n D.terms.post.stop.n.log
## 1516 1516
## D.weight.post.stop.sum D.terms.post.stem.n
## 1516 1516
## D.terms.post.stem.n.log D.weight.post.stem.sum
## 1516 1516
## D.T.condit D.T.use
## 2156 2363
## D.T.in. D.T.good
## 2216 2451
## D.T.screen D.T.with
## 2440 2434
## D.T.of D.T.mint
## 2497 2585
## D.T.or D.T.cosmet
## 2523 2531
## D.T.minor D.T.light
## 2531 2567
## D.T.X100 D.T.from
## 2584 2591
## D.T.hous D.wrds.n.log
## 2576 1513
## D.wrds.unq.n.log D.weight.sum
## 1516 1516
## D.ratio.weight.sum.wrds.n D.chrs.n.log
## 1516 1513
## D.chrs.uppr.n.log D.dgts.n.log
## 1515 2452
## D.chrs.pnct01.n.log D.chrs.pnct02.n.log
## 2570 2648
## D.chrs.pnct03.n.log D.chrs.pnct04.n.log
## 2647 2648
## D.chrs.pnct05.n.log D.chrs.pnct06.n.log
## 2582 2596
## D.chrs.pnct07.n.log D.chrs.pnct08.n.log
## 2611 2572
## D.chrs.pnct09.n.log D.chrs.pnct10.n.log
## 2632 2639
## D.chrs.pnct11.n.log D.chrs.pnct12.n.log
## 2294 2534
## D.chrs.pnct13.n.log D.chrs.pnct14.n.log
## 1930 2574
## D.chrs.pnct15.n.log D.chrs.pnct16.n.log
## 2628 2639
## D.chrs.pnct17.n.log D.chrs.pnct18.n.log
## 2646 2647
## D.chrs.pnct19.n.log D.chrs.pnct20.n.log
## 2648 2648
## D.chrs.pnct21.n.log D.chrs.pnct22.n.log
## 2648 2648
## D.chrs.pnct23.n.log D.chrs.pnct24.n.log
## 2648 2648
## D.chrs.pnct25.n.log D.chrs.pnct26.n.log
## 2648 2648
## D.chrs.pnct27.n.log D.chrs.pnct28.n.log
## 2648 2640
## D.chrs.pnct29.n.log D.chrs.pnct30.n.log
## 2648 2648
## D.wrds.stop.n.log D.P.http
## 1965 2648
## D.P.mini D.P.air
## 2615 2627
## D.P.black D.P.white
## 2631 2638
## D.P.gold D.P.spacegray
## 2647 2642
## startprice.dgt1.is9 startprice.dgt2.is9
## 1779 2290
## startprice.dgt3.is9 startprice.dcm1.is9
## 2643 1653
## startprice.dcm2.is9
## 1826
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1513 0 0 0 0 0
## productline .grpid descr.my
## 0 NA 1513# glb_allobs_df <- na.omit(glb_allobs_df)
# Not refactored into mydsutils.R since glb_*_df might be reassigned
glb_impute_missing_data <- function() {
require(mice)
set.seed(glb_mice_complete.seed)
inp_impent_df <- glb_allobs_df[, setdiff(names(glb_allobs_df),
union(glbFeatsExclude, glb_rsp_var))]
print("Summary before imputation: ")
print(summary(inp_impent_df))
out_impent_df <- complete(mice(inp_impent_df))
print(summary(out_impent_df))
ret_vars <- sapply(names(out_impent_df),
function(col) ifelse(!identical(out_impent_df[, col],
inp_impent_df[, col]),
col, ""))
ret_vars <- ret_vars[ret_vars != ""]
# complete(mice()) changes attributes of factors even though values don't change
for (col in ret_vars) {
if (inherits(out_impent_df[, col], "factor")) {
if (identical(as.numeric(out_impent_df[, col]),
as.numeric(inp_impent_df[, col])))
ret_vars <- setdiff(ret_vars, col)
}
}
return(out_impent_df[, ret_vars])
}
if (glb_impute_na_data &&
(length(myfind_numerics_missing(glb_allobs_df)) > 0) &&
(ncol(nonna_df <- glb_impute_missing_data()) > 0)) {
for (col in names(nonna_df)) {
glb_allobs_df[, paste0(col, ".nonNA")] <- nonna_df[, col]
glbFeatsExclude <- c(glbFeatsExclude, col)
}
}
mycheck_problem_data(glb_allobs_df, featsExclude = glbFeatsExclude,
fctrMaxUniqVals = glbFctrMaxUniqVals, terminate = TRUE)## [1] "numeric data missing in : "
## sold sold.fctr
## 798 798
## [1] "numeric data w/ 0s in : "
## biddable sold
## 1437 995
## sprice.log10 cellular.fctr
## 31 1590
## D.terms.post.stop.n D.terms.post.stop.n.log
## 1516 1516
## D.weight.post.stop.sum D.terms.post.stem.n
## 1516 1516
## D.terms.post.stem.n.log D.weight.post.stem.sum
## 1516 1516
## D.T.condit D.T.use
## 2156 2363
## D.T.in. D.T.good
## 2216 2451
## D.T.screen D.T.with
## 2440 2434
## D.T.of D.T.mint
## 2497 2585
## D.T.or D.T.cosmet
## 2523 2531
## D.T.minor D.T.light
## 2531 2567
## D.T.X100 D.T.from
## 2584 2591
## D.T.hous D.wrds.n.log
## 2576 1513
## D.wrds.unq.n.log D.weight.sum
## 1516 1516
## D.ratio.weight.sum.wrds.n D.chrs.n.log
## 1516 1513
## D.chrs.uppr.n.log D.dgts.n.log
## 1515 2452
## D.chrs.pnct01.n.log D.chrs.pnct02.n.log
## 2570 2648
## D.chrs.pnct03.n.log D.chrs.pnct04.n.log
## 2647 2648
## D.chrs.pnct05.n.log D.chrs.pnct06.n.log
## 2582 2596
## D.chrs.pnct07.n.log D.chrs.pnct08.n.log
## 2611 2572
## D.chrs.pnct09.n.log D.chrs.pnct10.n.log
## 2632 2639
## D.chrs.pnct11.n.log D.chrs.pnct12.n.log
## 2294 2534
## D.chrs.pnct13.n.log D.chrs.pnct14.n.log
## 1930 2574
## D.chrs.pnct15.n.log D.chrs.pnct16.n.log
## 2628 2639
## D.chrs.pnct17.n.log D.chrs.pnct18.n.log
## 2646 2647
## D.chrs.pnct19.n.log D.chrs.pnct20.n.log
## 2648 2648
## D.chrs.pnct21.n.log D.chrs.pnct22.n.log
## 2648 2648
## D.chrs.pnct23.n.log D.chrs.pnct24.n.log
## 2648 2648
## D.chrs.pnct25.n.log D.chrs.pnct26.n.log
## 2648 2648
## D.chrs.pnct27.n.log D.chrs.pnct28.n.log
## 2648 2640
## D.chrs.pnct29.n.log D.chrs.pnct30.n.log
## 2648 2648
## D.wrds.stop.n.log D.P.http
## 1965 2648
## D.P.mini D.P.air
## 2615 2627
## D.P.black D.P.white
## 2631 2638
## D.P.gold D.P.spacegray
## 2647 2642
## startprice.dgt1.is9 startprice.dgt2.is9
## 1779 2290
## startprice.dgt3.is9 startprice.dcm1.is9
## 2643 1653
## startprice.dcm2.is9
## 1826
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1513 0 0 0 0 0
## productline .grpid descr.my
## 0 NA 1513Step 3.1: manage missing data
glb_chunks_df <- myadd_chunk(glb_chunks_df, "cluster.data", major.inc=FALSE)## label step_major step_minor label_minor bgn end
## 6 manage.missing.data 3 1 1 250.784 250.94
## 7 cluster.data 3 2 2 250.941 NA
## elapsed
## 6 0.157
## 7 NAmycompute_entropy_df <- function(obs_df, entropy_var, by_var=NULL) {
require(lazyeval)
require(dplyr)
require(tidyr)
if (is.null(by_var)) {
by_var <- ".default"
obs_df$.default <- as.factor(".default")
}
if (!any(grepl(".clusterid", names(obs_df), fixed=TRUE)))
obs_df$.clusterid <- 1
cluster_df <- obs_df %>%
count_(c(by_var, ".clusterid", entropy_var)) %>%
dplyr::filter(n > 0) %>%
dplyr::filter_(interp(~(!is.na(var)), var=as.name(entropy_var))) %>%
unite_(paste0(by_var, ".clusterid"),
c(interp(by_var), ".clusterid")) %>%
spread_(interp(entropy_var), "n", fill=0)
# head(cluster_df)
# sum(cluster_df$n)
tmp.entropy <- sapply(1:nrow(cluster_df),
function(row) entropy(as.numeric(cluster_df[row, -1]), method = "ML"))
tmp.knt <- sapply(1:nrow(cluster_df),
function(row) sum(as.numeric(cluster_df[row, -1])))
cluster_df$.entropy <- tmp.entropy; cluster_df$.knt <- tmp.knt
#print(cluster_df)
return(cluster_df)
}
if (glb_cluster) {
require(proxy)
#require(hash)
require(dynamicTreeCut)
require(entropy)
require(tidyr)
require(ggdendro)
mywgtdcosine_dist <- function(x, y=NULL, weights=NULL) {
if (!inherits(x, "matrix"))
x <- as.matrix(x)
if (is.null(weights))
weights <- rep(1, ncol(x))
wgtsx <- matrix(rep(weights / sum(weights), nrow(x)), nrow = nrow(x),
byrow = TRUE)
wgtdx <- x * wgtsx
wgtdxsqsum <- as.matrix(rowSums((x ^ 2) * wgtsx), byrow=FALSE)
denom <- sqrt(wgtdxsqsum %*% t(wgtdxsqsum))
ret_mtrx <- 1 - ((sum(weights) ^ 1) * (wgtdx %*% t(wgtdx)) / denom)
ret_mtrx[is.nan(ret_mtrx)] <- 1
diag(ret_mtrx) <- 0
return(ret_mtrx)
}
#pr_DB$delete_entry("mywgtdcosine");
# Need to do this only once across runs ?
if (!pr_DB$entry_exists("mywgtdcosine")) {
pr_DB$set_entry(FUN = mywgtdcosine_dist, names = c("mywgtdcosine"))
pr_DB$modify_entry(names="mywgtdcosine", type="metric", loop=FALSE)
}
#pr_DB$get_entry("mywgtdcosine")
# glb_hash <- hash(key=unique(glb_allobs_df$myCategory),
# values=1:length(unique(glb_allobs_df$myCategory)))
# glb_hash_lst <- hash(key=unique(glb_allobs_df$myCategory),
# values=1:length(unique(glb_allobs_df$myCategory)))
#stop(here"); glb_to_sav(); glb_allobs_df <- sav_allobs_df
cluster_vars <- grep(paste0("[",
toupper(paste0(substr(glbFeatsText, 1, 1), collapse = "")),
"]\\.[PT]\\."),
names(glb_allobs_df), value = TRUE)
# Assign correlations with rsp_var as weights for cosine distance
print("Clustering features: ")
cluster_vars_df <- data.frame(abs.cor.y = abs(cor(
glb_allobs_df[glb_allobs_df$.src == "Train", cluster_vars],
as.numeric(glb_allobs_df[glb_allobs_df$.src == "Train", glb_rsp_var]),
use = "pairwise.complete.obs")))
print(tail(cluster_vars_df <- orderBy(~ abs.cor.y,
subset(cluster_vars_df, !is.na(abs.cor.y))), 5))
print(sprintf(" .rnorm cor: %0.4f",
cor(glb_allobs_df[glb_allobs_df$.src == "Train", ".rnorm"],
as.numeric(glb_allobs_df[glb_allobs_df$.src == "Train", glb_rsp_var]),
use = "pairwise.complete.obs")))
print(sprintf("glb_allobs_df Entropy: %0.4f",
allobs_ent <- entropy(table(glb_allobs_df[, glb_cluster_entropy_var]),
method="ML")))
print(category_df <- mycompute_entropy_df(obs_df=glb_allobs_df,
entropy_var=glb_cluster_entropy_var,
by_var=glb_category_var))
print(sprintf("glb_allobs_df$%s Entropy: %0.4f (%0.4f pct)",
glb_category_var,
category_ent <- weighted.mean(category_df$.entropy, category_df$.knt),
100 * category_ent / allobs_ent))
glb_allobs_df$.clusterid <- 1
#print(max(table(glb_allobs_df$myCategory.fctr) / 20))
#stop(here"); glb_to_sav()
grp_ids <- sort(unique(glb_allobs_df[, glb_category_var]))
glb_cluster_size_df_lst <- list()
png(paste0(glb_out_pfx, "FeatsTxtClusters.png"),
width = 480 * 2, height = 480 * length(grp_ids))
grid.newpage()
pushViewport(viewport(layout = grid.layout(nrow = length(grp_ids), ncol = 2)))
pltIx <- 1
for (grp in grp_ids) {
# if (grep(grp, levels(grp_ids)) <= 6) next
# if (grep(grp, levels(grp_ids)) > 9) next
# if (grep(grp, levels(grp_ids)) != 10) next
print(sprintf("Category: %s", grp))
ctgry_allobs_df <- glb_allobs_df[glb_allobs_df[, glb_category_var] == grp, ]
if (!inherits(ctgry_allobs_df[, glb_cluster_entropy_var], "factor"))
ctgry_allobs_df[, glb_cluster_entropy_var] <-
as.factor(ctgry_allobs_df[, glb_cluster_entropy_var])
#dstns_dist <- proxy::dist(ctgry_allobs_df[, cluster_vars], method = "cosine")
dstns_dist <- proxy::dist(ctgry_allobs_df[, row.names(cluster_vars_df)],
method = "mywgtdcosine",
weights = cluster_vars_df$abs.cor.y)
# Custom distance functions return a crossdist object
#dstns_mtrx <- as.matrix(dstns_dist)
dstns_mtrx <- matrix(as.vector(dstns_dist), nrow=attr(dstns_dist, "dim")[1],
dimnames=attr(dstns_dist, "dimnames"))
dstns_dist <- as.dist(dstns_mtrx)
print(sprintf("max distance(%0.4f) pair:", max(dstns_mtrx)))
# print(dim(dstns_mtrx))
# print(sprintf("which.max: %d", which.max(dstns_mtrx)))
row_ix <- ceiling(which.max(dstns_mtrx) / ncol(dstns_mtrx))
col_ix <- which.max(dstns_mtrx[row_ix, ])
# print(sprintf("row_ix: %d", row_ix)); print(sprintf("col_ix: %d", col_ix));
# print(dim(ctgry_allobs_df))
print(ctgry_allobs_df[c(row_ix, col_ix),
c(glb_id_var, glb_cluster_entropy_var, glb_category_var, glbFeatsText, cluster_vars)])
min_dstns_mtrx <- dstns_mtrx
diag(min_dstns_mtrx) <- 1
# Float representations issue -2.22e-16 vs. 0.0000
print(sprintf("min distance(%0.4f) pair:", min(min_dstns_mtrx)))
row_ix <- ceiling(which.min(min_dstns_mtrx) / ncol(min_dstns_mtrx))
col_ix <- which.min(min_dstns_mtrx[row_ix, ])
print(ctgry_allobs_df[c(row_ix, col_ix),
c(glb_id_var, glb_cluster_entropy_var, glb_category_var, glbFeatsText,
cluster_vars)])
set.seed(glb_cluster.seed)
clusters <- hclust(dstns_dist, method = "ward.D2")
# Workaround to avoid "Error in cutree(dendro, h = heightcutoff) : the 'height' component of 'tree' is not sorted (increasingly)"
if (with(clusters,all.equal(height,sort(height))))
clusters$height <- round(clusters$height,6)
clusters$labels <- ctgry_allobs_df[, glb_id_var]
clustersDD <- dendro_data(clusters)
clustersDD$labels[, glb_rsp_var] <- sapply(clustersDD$labels$label, function(id)
ctgry_allobs_df[id == ctgry_allobs_df[, glb_id_var], glb_rsp_var])
print(ggdendrogram(clustersDD, rotate = TRUE, size = 2) +
geom_point(data = clustersDD$labels,
aes_string(x = "x", color = glb_rsp_var), y = min(clustersDD$segments$y)) +
coord_flip(ylim = c(min(clustersDD$segments$y),
max(clustersDD$segments$y))) +
ggtitle(grp),
vp = viewport(layout.pos.row = pltIx, layout.pos.col = 1))
# clusters$labels <- ctgry_allobs_df[, glb_id_var]
# clustersDD <- dendro_data(clusters)
# clustersDD$labels$color <- sapply(clustersDD$labels$label, function(id)
# ctgry_allobs_df[id == ctgry_allobs_df[, glb_id_var], glb_rsp_var])
# print(ggdendrogram(clustersDD, rotate = TRUE, size = 2) +
# geom_point(data = clustersDD$labels,
# aes_string(x = "x", color = "color"), y = min(clustersDD$segments$y)) +
# coord_flip(ylim = c(min(clustersDD$segments$y),
# max(clustersDD$segments$y))))
# print(ggdendrogram(clustersDD, rotate = TRUE, size = 2) +
# geom_point(data = clustersDD$labels,
# aes_string(x = "x", y = "y", color = "color")))
# myplclust(clusters, lab=ctgry_allobs_df[, glb_id_var],
# lab.col=unclass(ctgry_allobs_df[, glb_cluster_entropy_var]))
opt_minclustersize_df <- data.frame(minclustersize = nrow(ctgry_allobs_df),
entropy = entropy(table(ctgry_allobs_df[, glb_cluster_entropy_var]),
method = "ML"))
for (minclustersize in
as.integer(seq(nrow(ctgry_allobs_df) / 2, nrow(ctgry_allobs_df) / 10,
length = 5))) {
clusterGroups <- cutreeDynamic(clusters, minClusterSize = minclustersize,
method = "tree", deepSplit = 0)
# Unassigned groups are labeled 0; the largest group has label 1
clusterGroups[clusterGroups == 0] <- 1
ctgry_allobs_df$.clusterid <- clusterGroups
ctgry_clstrs_df <- mycompute_entropy_df(ctgry_allobs_df,
glb_cluster_entropy_var)
opt_minclustersize_df <- rbind(opt_minclustersize_df,
data.frame(minclustersize = minclustersize,
entropy = weighted.mean(ctgry_clstrs_df$.entropy, ctgry_clstrs_df$.knt)))
}
opt_minclustersize <-
opt_minclustersize_df$minclustersize[which.min(opt_minclustersize_df$entropy)]
opt_minclustersize_df$.color <-
ifelse(opt_minclustersize_df$minclustersize == opt_minclustersize,
"red", "blue")
print(ggplot(data = opt_minclustersize_df,
mapping = aes(x = minclustersize, y = entropy)) +
geom_point(aes(color = .color)) + scale_color_identity() +
guides(color = "none") + geom_line(),
vp = viewport(layout.pos.row = pltIx, layout.pos.col = 2))
glb_cluster_size_df_lst[[grp]] <- opt_minclustersize_df
# select minclustersize that minimizes entropy
clusterGroups <- cutreeDynamic(clusters, minClusterSize = opt_minclustersize,
method = "tree",
deepSplit = 0)
# Unassigned groups are labeled 0; the largest group has label 1
table(clusterGroups, ctgry_allobs_df[, glb_cluster_entropy_var],
useNA = "ifany")
clusterGroups[clusterGroups == 0] <- 1
table(clusterGroups, ctgry_allobs_df[, glb_cluster_entropy_var], useNA = "ifany")
glb_allobs_df[glb_allobs_df[, glb_category_var] == grp,]$.clusterid <-
clusterGroups
pltIx <- pltIx + 1
}
dev.off()
#all.equal(sav_allobs_df_clusterid, glb_allobs_df$.clusterid)
print(cluster_df <- mycompute_entropy_df(obs_df=glb_allobs_df,
entropy_var=glb_cluster_entropy_var,
by_var=glb_category_var))
print(sprintf("glb_allobs_df$%s$.clusterid Entropy: %0.4f (%0.4f pct)",
glb_category_var,
cluster_ent <- weighted.mean(cluster_df$.entropy, cluster_df$.knt),
100 * cluster_ent / category_ent))
glb_allobs_df$.clusterid.fctr <- as.factor(glb_allobs_df$.clusterid)
# .clusterid.fctr is created automatically (probably ?) later
glbFeatsExclude <- c(glbFeatsExclude, ".clusterid")
if (!is.null(glb_category_var))
# glb_interaction_only_feats_lst[ifelse(grepl("\\.fctr", glb_category_var),
# glb_category_var,
# paste0(glb_category_var, ".fctr"))] <-
# c(".clusterid.fctr")
glb_interaction_only_feats_lst[[".clusterid.fctr"]] <-
ifelse(grepl("\\.fctr", glb_category_var), glb_category_var,
paste0(glb_category_var, ".fctr"))
if (glbFeatsTextClusterVarsExclude)
glbFeatsExclude <- c(glbFeatsExclude, cluster_vars)
}## Loading required package: proxy
##
## Attaching package: 'proxy'
##
## The following objects are masked from 'package:stats':
##
## as.dist, dist
##
## The following object is masked from 'package:base':
##
## as.matrix
##
## Loading required package: dynamicTreeCut
## Loading required package: entropy
## Loading required package: ggdendro## [1] "Clustering features: "## Warning in cor(glb_allobs_df[glb_allobs_df$.src == "Train",
## cluster_vars], : the standard deviation is zero## abs.cor.y
## D.T.with 0.06608760
## D.T.in. 0.07165392
## D.T.cosmet 0.08848086
## D.T.X100 0.11139850
## D.T.hous 0.12927488
## [1] " .rnorm cor: -0.0013"
## [1] "glb_allobs_df Entropy: 0.6903"## Loading required package: lazyeval## Source: local data frame [10 x 5]
##
## prdl.my.fctr.clusterid N Y .entropy .knt
## (chr) (dbl) (dbl) (dbl) (dbl)
## 1 Unknown_1 122 82 0.6737987 204
## 2 iPad1_1 100 125 0.6869616 225
## 3 iPad2_1 139 147 0.6927559 286
## 4 iPad3_1 73 80 0.6921002 153
## 5 iPad4_1 93 64 0.6759893 157
## 6 iPadAir2_1 100 71 0.6786969 171
## 7 iPadAir_1 102 78 0.6842318 180
## 8 iPadmini2_1 58 49 0.6896056 107
## 9 iPadmini3_1 63 27 0.6108643 90
## 10 iPadmini_1 145 132 0.6920455 277
## [1] "glb_allobs_df$prdl.my.fctr Entropy: 0.6821 (98.8123 pct)"## [1] "Category: Unknown"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdl.my.fctr
## 5 10005 N Unknown
## 24 10024 N Unknown
## descr.my
## 5 Please feel free to buy. All products have been thoroughly inspected, cleaned and tested to be 100%
## 24
## D.T.condit D.T.use D.T.in. D.T.good D.T.screen D.T.with D.T.of D.T.mint
## 5 0 0 0 0 0 0 0 0
## 24 0 0 0 0 0 0 0 0
## D.T.or D.T.cosmet D.T.minor D.T.light D.T.X100 D.T.from D.T.hous
## 5 0 0 0 0 0.4475573 0 0
## 24 0 0 0 0 0.0000000 0 0
## D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 5 0 0 0 0 0 0 0
## 24 0 0 0 0 0 0 0
## [1] "min distance(0.8891) pair:"
## UniqueID sold.fctr prdl.my.fctr
## 5 10005 N Unknown
## 419 10420 Y Unknown
## descr.my
## 5 Please feel free to buy. All products have been thoroughly inspected, cleaned and tested to be 100%
## 419 THIS ITEM WAS A STORE DEMO BUT HAS 100% BEEN RESTORED TO FACTORY SETTINGS AND IS READY FOR USE. DOES
## D.T.condit D.T.use D.T.in. D.T.good D.T.screen D.T.with D.T.of
## 5 0 0.0000000 0 0 0 0 0
## 419 0 0.1891688 0 0 0 0 0
## D.T.mint D.T.or D.T.cosmet D.T.minor D.T.light D.T.X100 D.T.from
## 5 0 0 0 0 0 0.4475573 0
## 419 0 0 0 0 0 0.3159228 0
## D.T.hous D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold
## 5 0 0 0 0 0 0 0
## 419 0 0 0 0 0 0 0
## D.P.spacegray
## 5 0
## 419 0## Warning in max(vapply(evaled, length, integer(1))): no non-missing
## arguments to max; returning -Inf## [1] "Category: iPad1"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdl.my.fctr descr.my D.T.condit D.T.use D.T.in.
## 9 10009 Y iPad1 0 0 0
## 12 10012 N iPad1 0 0 0
## D.T.good D.T.screen D.T.with D.T.of D.T.mint D.T.or D.T.cosmet
## 9 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0
## D.T.minor D.T.light D.T.X100 D.T.from D.T.hous D.P.http D.P.mini
## 9 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0
## D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 9 0 0 0 0 0
## 12 0 0 0 0 0
## [1] "min distance(0.8976) pair:"
## UniqueID sold.fctr prdl.my.fctr
## 2290 12301 <NA> iPad1
## 2326 12337 <NA> iPad1
## descr.my
## 2290 A device listed in Average/Fair condition with some scratches, scuffs on the housing & screen (do
## 2326 A device listed in Average/Fair condition with some scratches, scuffs on the housing & screen (do
## D.T.condit D.T.use D.T.in. D.T.good D.T.screen D.T.with D.T.of
## 2290 0.1618782 0 0.1743867 0 0.2446832 0.241948 0
## 2326 0.1618782 0 0.1743867 0 0.2446832 0.241948 0
## D.T.mint D.T.or D.T.cosmet D.T.minor D.T.light D.T.X100 D.T.from
## 2290 0 0 0 0 0 0 0
## 2326 0 0 0 0 0 0 0
## D.T.hous D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold
## 2290 0.3467175 0 0 0 0 0 0
## 2326 0.3467175 0 0 0 0 0 0
## D.P.spacegray
## 2290 0
## 2326 0## Warning in max(vapply(evaled, length, integer(1))): no non-missing
## arguments to max; returning -Inf## [1] "Category: iPad2"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdl.my.fctr
## 1 10001 N iPad2
## 21 10021 Y iPad2
## descr.my D.T.condit D.T.use
## 1 iPad is in 8point5+ out of 10 cosmetic condition! 0.3035216 0
## 21 Crack on screen. 0.0000000 0
## D.T.in. D.T.good D.T.screen D.T.with D.T.of D.T.mint D.T.or
## 1 0.326975 0 0.000000 0 0.5165353 0 0
## 21 0.000000 0 1.223416 0 0.0000000 0 0
## D.T.cosmet D.T.minor D.T.light D.T.X100 D.T.from D.T.hous D.P.http
## 1 0.5625403 0 0 0 0 0 0
## 21 0.0000000 0 0 0 0 0 0
## D.P.mini D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 1 0 0 0 0 0 0
## 21 0 0 0 0 0 0
## [1] "min distance(0.8886) pair:"
## UniqueID sold.fctr prdl.my.fctr
## 431 10432 N iPad2
## 664 10665 N iPad2
## descr.my
## 431 *FREE* Same-Day Ship | Factory Refurbished | 90-Day Warranty | 100% Functional, Includes All Major
## 664 100% Fully Functional, Clean ESN & iCloud Clear, professionally restored, inspected, & tested. This
## D.T.condit D.T.use D.T.in. D.T.good D.T.screen D.T.with D.T.of
## 431 0 0 0 0 0 0 0
## 664 0 0 0 0 0 0 0
## D.T.mint D.T.or D.T.cosmet D.T.minor D.T.light D.T.X100 D.T.from
## 431 0 0 0 0 0 0.4882443 0
## 664 0 0 0 0 0 0.4882443 0
## D.T.hous D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold
## 431 0 0 0 0 0 0 0
## 664 0 0 0 0 0 0 0
## D.P.spacegray
## 431 0
## 664 0## Warning in max(vapply(evaled, length, integer(1))): no non-missing
## arguments to max; returning -Inf## [1] "No module detected"
## [1] "Category: iPad3"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdl.my.fctr
## 17 10017 Y iPad3
## 37 10037 Y iPad3
## descr.my
## 17 Great working iPad. Very minor surface scratches on back as pictured. Other very light scratching
## 37 Rarely ever used it.
## D.T.condit D.T.use D.T.in. D.T.good D.T.screen D.T.with D.T.of
## 17 0 0.000000 0 0 0 0 0
## 37 0 1.607935 0 0 0 0 0
## D.T.mint D.T.or D.T.cosmet D.T.minor D.T.light D.T.X100 D.T.from
## 17 0 0 0 0.3214516 0.3593455 0 0
## 37 0 0 0 0.0000000 0.0000000 0 0
## D.T.hous D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold
## 17 0 0 0 0 0 0 0
## 37 0 0 0 0 0 0 0
## D.P.spacegray
## 17 0
## 37 0
## [1] "min distance(0.8886) pair:"
## UniqueID sold.fctr prdl.my.fctr
## 946 10949 N iPad3
## 1910 11921 <NA> iPad3
## descr.my
## 946 *FREE* Same-Day Ship | 90-Day Warranty | 100% Functional, Includes All Major Accessories, Shows
## 1910 *FREE* Same-Day Ship | 90-Day Warranty | 100% Functional, Includes All Major Accessories, Shows
## D.T.condit D.T.use D.T.in. D.T.good D.T.screen D.T.with D.T.of
## 946 0 0 0 0 0 0 0
## 1910 0 0 0 0 0 0 0
## D.T.mint D.T.or D.T.cosmet D.T.minor D.T.light D.T.X100 D.T.from
## 946 0 0 0 0 0 0.4882443 0
## 1910 0 0 0 0 0 0.4882443 0
## D.T.hous D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold
## 946 0 0 0 0 0 0 0
## 1910 0 0 0 0 0 0 0
## D.P.spacegray
## 946 0
## 1910 0## Warning in max(vapply(evaled, length, integer(1))): no non-missing
## arguments to max; returning -Inf## [1] "Category: iPad4"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdl.my.fctr descr.my D.T.condit D.T.use D.T.in.
## 3 10003 Y iPad4 0 0 0
## 10 10010 Y iPad4 0 0 0
## D.T.good D.T.screen D.T.with D.T.of D.T.mint D.T.or D.T.cosmet
## 3 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0
## D.T.minor D.T.light D.T.X100 D.T.from D.T.hous D.P.http D.P.mini
## 3 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0
## D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 3 0 0 0 0 0
## 10 0 0 0 0 0
## [1] "min distance(0.8886) pair:"
## UniqueID sold.fctr prdl.my.fctr
## 63 10063 N iPad4
## 2308 12319 <NA> iPad4
## descr.my
## 63 *FREE* Same-Day Ship | 90-Day Warranty | 100% Functional, Includes All Major Accessories, Shows
## 2308 *FREE* Same-Day Ship | 90-Day Warranty | 100% Functional, Includes All Major Accessories, Shows
## D.T.condit D.T.use D.T.in. D.T.good D.T.screen D.T.with D.T.of
## 63 0 0 0 0 0 0 0
## 2308 0 0 0 0 0 0 0
## D.T.mint D.T.or D.T.cosmet D.T.minor D.T.light D.T.X100 D.T.from
## 63 0 0 0 0 0 0.4882443 0
## 2308 0 0 0 0 0 0.4882443 0
## D.T.hous D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold
## 63 0 0 0 0 0 0 0
## 2308 0 0 0 0 0 0 0
## D.P.spacegray
## 63 0
## 2308 0## Warning in max(vapply(evaled, length, integer(1))): no non-missing
## arguments to max; returning -Inf## [1] "Category: iPadAir"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdl.my.fctr descr.my D.T.condit D.T.use D.T.in.
## 16 10016 N iPadAir 0 0 0
## 25 10025 N iPadAir 0 0 0
## D.T.good D.T.screen D.T.with D.T.of D.T.mint D.T.or D.T.cosmet
## 16 0 0 0 0 0 0 0
## 25 0 0 0 0 0 0 0
## D.T.minor D.T.light D.T.X100 D.T.from D.T.hous D.P.http D.P.mini
## 16 0 0 0 0 0 0 0
## 25 0 0 0 0 0 0 0
## D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 16 0 0 0 0 0
## 25 0 0 0 0 0
## [1] "min distance(0.8886) pair:"
## UniqueID sold.fctr prdl.my.fctr
## 167 10167 N iPadAir
## 1156 11162 N iPadAir
## descr.my
## 167 LIKE BRANDNEW 100% MONEY BACK GUARANTEE
## 1156 *FREE* Same-Day Ship | 90-Day Warranty | 100% Functional, Includes All Major Accessories, Shows
## D.T.condit D.T.use D.T.in. D.T.good D.T.screen D.T.with D.T.of
## 167 0 0 0 0 0 0 0
## 1156 0 0 0 0 0 0 0
## D.T.mint D.T.or D.T.cosmet D.T.minor D.T.light D.T.X100 D.T.from
## 167 0 0 0 0 0 1.7902291 0
## 1156 0 0 0 0 0 0.4882443 0
## D.T.hous D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold
## 167 0 0 0 0 0 0 0
## 1156 0 0 0 0 0 0 0
## D.P.spacegray
## 167 0
## 1156 0## Warning in max(vapply(evaled, length, integer(1))): no non-missing
## arguments to max; returning -Inf## [1] "Category: iPadAir2"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdl.my.fctr descr.my D.T.condit D.T.use D.T.in.
## 19 10019 Y iPadAir2 0 0 0
## 35 10035 N iPadAir2 0 0 0
## D.T.good D.T.screen D.T.with D.T.of D.T.mint D.T.or D.T.cosmet
## 19 0 0 0 0 0 0 0
## 35 0 0 0 0 0 0 0
## D.T.minor D.T.light D.T.X100 D.T.from D.T.hous D.P.http D.P.mini
## 19 0 0 0 0 0 0 0
## 35 0 0 0 0 0 0 0
## D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 19 0 0 0 0 0
## 35 0 0 0 0 0
## [1] "min distance(0.9283) pair:"
## UniqueID sold.fctr prdl.my.fctr
## 1214 11220 N iPadAir2
## 1856 11867 <NA> iPadAir2
## descr.my
## 1214 Brandnew, iPad untouched and still wrapped in original plastic inside box. Opened only to verify
## 1856 Brandnew factory sealed iPad in an OPEN BOX...THE BOX ITSELF IS HEAVILY DISTRESSED(see
## D.T.condit D.T.use D.T.in. D.T.good D.T.screen D.T.with D.T.of
## 1214 0 0 0.2378000 0 0 0 0
## 1856 0 0 0.2012154 0 0 0 0
## D.T.mint D.T.or D.T.cosmet D.T.minor D.T.light D.T.X100 D.T.from
## 1214 0 0 0 0 0 0 0
## 1856 0 0 0 0 0 0 0
## D.T.hous D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold
## 1214 0 0 0 0 0 0 0
## 1856 0 0 0 0 0 0 0
## D.P.spacegray
## 1214 0
## 1856 0## Warning in max(vapply(evaled, length, integer(1))): no non-missing
## arguments to max; returning -Inf## [1] "No module detected"
## [1] "Category: iPadmini"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdl.my.fctr descr.my D.T.condit D.T.use D.T.in.
## 7 10007 Y iPadmini 0 0 0
## 57 10057 N iPadmini 0 0 0
## D.T.good D.T.screen D.T.with D.T.of D.T.mint D.T.or D.T.cosmet
## 7 0 0 0 0 0 0 0
## 57 0 0 0 0 0 0 0
## D.T.minor D.T.light D.T.X100 D.T.from D.T.hous D.P.http D.P.mini
## 7 0 0 0 0 0 0 0
## 57 0 0 0 0 0 0 0
## D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 7 0 0 0 0 0
## 57 0 0 0 0 0
## [1] "min distance(0.8919) pair:"
## UniqueID sold.fctr prdl.my.fctr
## 93 10093 N iPadmini
## 2364 12375 <NA> iPadmini
## descr.my
## 93 *FREE* Same-Day Ship | 90-Day Warranty | 100% Functional, Includes All Major Accessories, Shows
## 2364 100% working condition, No icloud blocks, tablet is ready for new user. See item description for
## D.T.condit D.T.use D.T.in. D.T.good D.T.screen D.T.with D.T.of
## 93 0.0000000 0 0 0 0 0 0
## 2364 0.1734409 0 0 0 0 0 0
## D.T.mint D.T.or D.T.cosmet D.T.minor D.T.light D.T.X100 D.T.from
## 93 0 0 0 0 0 0.4882443 0
## 2364 0 0 0 0 0 0.3836205 0
## D.T.hous D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold
## 93 0 0 0 0 0 0 0
## 2364 0 0 0 0 0 0 0
## D.P.spacegray
## 93 0
## 2364 0## Warning in max(vapply(evaled, length, integer(1))): no non-missing
## arguments to max; returning -Inf## [1] "No module detected"
## [1] "Category: iPadmini2"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdl.my.fctr descr.my D.T.condit D.T.use D.T.in.
## 4 10004 N iPadmini2 0 0 0
## 6 10006 Y iPadmini2 0 0 0
## D.T.good D.T.screen D.T.with D.T.of D.T.mint D.T.or D.T.cosmet D.T.minor
## 4 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## D.T.light D.T.X100 D.T.from D.T.hous D.P.http D.P.mini D.P.air D.P.black
## 4 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## D.P.white D.P.gold D.P.spacegray
## 4 0 0 0
## 6 0 0 0
## [1] "min distance(0.9126) pair:"
## UniqueID sold.fctr prdl.my.fctr
## 927 10930 N iPadmini2
## 2020 12031 <NA> iPadmini2
## descr.my
## 927 Flawless Condition(10/10) 100% functional with Flawless Retina Display. Unit is free of
## 2020 Good Condition(8point25/10), 100% functional with Flawless Retina Display. Unit has a dent on upper left
## D.T.condit D.T.use D.T.in. D.T.good D.T.screen D.T.with D.T.of
## 927 0.1867825 0 0 0.0000000 0 0.2791708 0.3178679
## 2020 0.1734409 0 0 0.2677597 0 0.2592300 0.0000000
## D.T.mint D.T.or D.T.cosmet D.T.minor D.T.light D.T.X100 D.T.from
## 927 0 0 0 0 0 0.4131298 0
## 2020 0 0 0 0 0 0.3836205 0
## D.T.hous D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold
## 927 0 0 0 0 0 0 0
## 2020 0 0 0 0 0 0 0
## D.P.spacegray
## 927 0
## 2020 0## Warning in max(vapply(evaled, length, integer(1))): no non-missing
## arguments to max; returning -Inf## [1] "Category: iPadmini3"
## [1] "max distance(1.0000) pair:"
## UniqueID sold.fctr prdl.my.fctr descr.my D.T.condit D.T.use D.T.in.
## 8 10008 N iPadmini3 0 0 0
## 120 10120 N iPadmini3 0 0 0
## D.T.good D.T.screen D.T.with D.T.of D.T.mint D.T.or D.T.cosmet
## 8 0 0 0 0 0 0 0
## 120 0 0 0 0 0 0 0
## D.T.minor D.T.light D.T.X100 D.T.from D.T.hous D.P.http D.P.mini
## 8 0 0 0 0 0 0 0
## 120 0 0 0 0 0 0 0
## D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 8 0 0 0 0 0
## 120 0 0 0 0 0
## [1] "min distance(0.9488) pair:"
## UniqueID sold.fctr prdl.my.fctr
## 1011 11014 N iPadmini3
## 1115 11121 N iPadmini3
## descr.my
## 1011 Brandnew IpadMini, still in plastic wrap and box provided by hospital with cords and new unopened
## 1115 Bought for one day and removed iPad plastic cover, never used. In original box with all accessories!
## D.T.condit D.T.use D.T.in. D.T.good D.T.screen D.T.with D.T.of
## 1011 0 0.0000000 0.2179833 0 0 0.302435 0
## 1115 0 0.2143913 0.1743867 0 0 0.241948 0
## D.T.mint D.T.or D.T.cosmet D.T.minor D.T.light D.T.X100 D.T.from
## 1011 0 0 0 0 0 0 0
## 1115 0 0 0 0 0 0 0
## D.T.hous D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold
## 1011 0 0 1 0 0 0 0
## 1115 0 0 0 0 0 0 0
## D.P.spacegray
## 1011 0
## 1115 0## Warning in max(vapply(evaled, length, integer(1))): no non-missing
## arguments to max; returning -Inf## [1] "No module detected"
## Source: local data frame [19 x 5]
##
## prdl.my.fctr.clusterid N Y .entropy .knt
## (chr) (dbl) (dbl) (dbl) (dbl)
## 1 Unknown_1 92 62 0.6740508 154
## 2 Unknown_2 17 12 0.6782094 29
## 3 Unknown_3 13 8 0.6645284 21
## 4 iPad1_1 72 104 0.6765260 176
## 5 iPad1_2 15 12 0.6869616 27
## 6 iPad1_3 13 9 0.6765260 22
## 7 iPad2_1 139 147 0.6927559 286
## 8 iPad3_1 60 75 0.6869616 135
## 9 iPad3_2 13 5 0.5908422 18
## 10 iPad4_1 52 52 0.6931472 104
## 11 iPad4_2 16 12 0.6829081 28
## 12 iPad4_3 25 0 0.0000000 25
## 13 iPadAir2_1 100 71 0.6786969 171
## 14 iPadAir_1 86 60 0.6772057 146
## 15 iPadAir_2 16 18 0.6914161 34
## 16 iPadmini2_1 50 44 0.6911087 94
## 17 iPadmini2_2 8 5 0.6662784 13
## 18 iPadmini3_1 63 27 0.6108643 90
## 19 iPadmini_1 145 132 0.6920455 277
## [1] "glb_allobs_df$prdl.my.fctr$.clusterid Entropy: 0.6710 (98.3773 pct)"# Last call for data modifications
#stop(here") # sav_allobs_df <- glb_allobs_df
# glb_allobs_df[(glb_allobs_df$PropR == 0.75) & (glb_allobs_df$State == "Hawaii"), "PropR.fctr"] <- "N"
# Re-partition
glb_trnobs_df <- subset(glb_allobs_df, .src == "Train")
glb_newobs_df <- subset(glb_allobs_df, .src == "Test")
glb_chunks_df <- myadd_chunk(glb_chunks_df, "partition.data.training", major.inc=TRUE)## label step_major step_minor label_minor bgn
## 7 cluster.data 3 2 2 250.941
## 8 partition.data.training 4 0 0 299.110
## end elapsed
## 7 299.109 48.169
## 8 NA NAStep 4.0: partition data training
#stop(here"); glb_to_sav()
if (all(is.na(glb_newobs_df[, glb_rsp_var]))) {
set.seed(glb_split_sample.seed)
OOB_size <- nrow(glb_newobs_df) * 1.1
if (is.null(glb_category_var)) {
require(caTools)
split <- sample.split(glb_trnobs_df[, glb_rsp_var_raw],
SplitRatio=OOB_size / nrow(glb_trnobs_df))
glb_OOBobs_df <- glb_trnobs_df[split ,]
glb_fitobs_df <- glb_trnobs_df[!split, ]
} else {
sample_vars <- c(glb_category_var, glb_rsp_var_raw)
rspvar_freq_df <- orderBy(reformulate(glb_rsp_var_raw),
mycreate_sqlxtab_df(glb_trnobs_df, glb_rsp_var_raw))
OOB_rspvar_size <-
1.0 * OOB_size * rspvar_freq_df$.n / sum(rspvar_freq_df$.n)
names(OOB_rspvar_size) <- as.character(rspvar_freq_df[, glb_rsp_var_raw])
newobs_freq_df <- orderBy(reformulate(glb_category_var),
mycreate_sqlxtab_df(glb_newobs_df, glb_category_var))
trnobs_freq_df <- orderBy(reformulate(glb_category_var),
mycreate_sqlxtab_df(glb_trnobs_df, glb_category_var))
ctgry_freq_df <- merge(newobs_freq_df, trnobs_freq_df,
by = glb_category_var,
all = TRUE, sort = TRUE,
suffixes = c(".tst", ".trn"))
ctgry_freq_df[is.na(ctgry_freq_df)] <- 0
obs_freq_df <- mycreate_xtab_df(glb_trnobs_df,
c(glb_category_var, glb_rsp_var_raw))
newobs_freq_df <- orderBy(reformulate(glb_category_var),
mycreate_sqlxtab_df(glb_newobs_df, glb_category_var))
names(newobs_freq_df) <- gsub(".n", ".n.tst", names(newobs_freq_df),
fixed = TRUE)
obs_freq_df <- merge(obs_freq_df, newobs_freq_df, all = TRUE)
strata_mtrx <- ceiling(
matrix(obs_freq_df$.n.tst * 1.0 / sum(obs_freq_df$.n.tst)) %*%
matrix(OOB_rspvar_size, nrow = 1))
dimnames(strata_mtrx)[[1]] <- obs_freq_df[, glb_category_var]
dimnames(strata_mtrx)[[2]] <- as.character(rspvar_freq_df[, glb_rsp_var_raw])
for (val in rspvar_freq_df[, glb_rsp_var_raw]) {
trn <- paste0(glb_rsp_var_raw, ".", as.character(val))
strata <- paste0(".strata.", as.character(val))
obs_freq_df[, strata] <- strata_mtrx[, as.character(val)]
if (length((ix <- which(obs_freq_df[, trn] <
2.0 * obs_freq_df[, strata]))) > 0)
# More obs in OOB compared to fit currently
obs_freq_df[ix, strata] <- floor(obs_freq_df[ix, trn] / 2.0)
if (length((ix <- which(obs_freq_df[, strata] == 0))) > 0)
obs_freq_df[ix, strata] <- 1
}
#print(colSums(obs_freq_df[, -1]))
# obs_freq_df <- expand.grid(
# glb_category_var = ctgry_freq_df[, glb_category_var],
# glb_rsp_var_raw = rspvar_freq_df[, glb_rsp_var_raw])
# names(obs_freq_df) <- sample_vars
# trnobs_freq_df <- orderBy(reformulate(sample_vars),
# mycreate_sqlxtab_df(glb_trnobs_df, sample_vars))
# names(trnobs_freq_df) <- gsub(".n", ".n.trn", names(trnobs_freq_df),
# fixed = TRUE)
# obs_freq_df <- merge(obs_freq_df, trnobs_freq_df, all = TRUE)
# newobs_freq_df <- orderBy(reformulate(glb_category_var),
# mycreate_sqlxtab_df(glb_newobs_df, glb_category_var))
# # Since glb_rsp_var_raw might be NA in newobs_df, repeat .n.Test across unique values of glb_rsp_var_raw to prep obs_freq_df
# newobs_rsp_freq_df <- data.frame()
# for (val in rspvar_freq_df[, glb_rsp_var_raw])
# newobs_rsp_freq_df <- rbind(newobs_rsp_freq_df,
# cbind(newobs_freq_df, data.frame(glb_rsp_var_raw = val)))
# obs_freq_df <- merge(obs_freq_df, newobs_freq_df, all = TRUE)
# obs_freq_df <- orderBy(reformulate(sample_vars), obs_freq_df)
#
# obs_freq_df$.n.strata <- ceiling(
# as.vector(matrix(ctgry_freq_df$.n.Tst * 1.0 /
# sum(ctgry_freq_df$.n.Tst)) %*%
# matrix(OOB_rspvar_size, nrow = 1)))
# obs_freq_df[obs_freq_df$.n.Strata == 0, ".n.Strata"] <- 1
#
# # Adjust OOB_strata_size (desired # of OOB obs) if > # of trn obs
# ix <- which(!is.na(trnobs_univ_df$.n) &
# (OOB_strata_size > trnobs_univ_df$.n))
# if (length(ix) > 0)
# OOB_strata_size[ix] <- trnobs_univ_df[ix, ".n"]
# # Adjust OOB_strata_size (desired # of OOB obs) if > # of (trn - OOB) obs
# ix <- which(!is.na(trnobs_univ_df$.n) &
# (OOB_strata_size * 2 > trnobs_univ_df$.n))
# if (length(ix) > 0)
# OOB_strata_size[ix] <- trnobs_univ_df[ix, ".n"]
OOB_strata_size <- as.vector(as.matrix(obs_freq_df[,
grepl("^\\.strata\\.", names(obs_freq_df))]))
tmp_trnobs_df <- orderBy(reformulate(c(glb_rsp_var_raw, glb_category_var)),
glb_trnobs_df)
require(sampling)
split_strata <- sampling::strata(tmp_trnobs_df,
stratanames = c(glb_rsp_var_raw, glb_category_var),
size = OOB_strata_size[!is.na(OOB_strata_size)],
method = "srswor")
glb_OOBobs_df <- getdata(tmp_trnobs_df, split_strata)[, names(glb_trnobs_df)]
glb_fitobs_df <- glb_trnobs_df[!glb_trnobs_df[, glb_id_var] %in%
glb_OOBobs_df[, glb_id_var], ]
}
} else {
print(sprintf("Newdata contains non-NA data for %s; setting OOB to Newdata",
glb_rsp_var))
glb_fitobs_df <- glb_trnobs_df; glb_OOBobs_df <- glb_newobs_df
}## Loading required package: sampling
##
## Attaching package: 'sampling'
##
## The following objects are masked from 'package:survival':
##
## cluster, strata
##
## The following object is masked from 'package:caret':
##
## clusterif (!is.null(glb_max_fitobs) && (nrow(glb_fitobs_df) > glb_max_fitobs)) {
warning("glb_fitobs_df restricted to glb_max_fitobs: ",
format(glb_max_fitobs, big.mark = ","))
org_fitobs_df <- glb_fitobs_df
glb_fitobs_df <-
org_fitobs_df[split <- sample.split(org_fitobs_df[, glb_rsp_var_raw],
SplitRatio = glb_max_fitobs), ]
org_fitobs_df <- NULL
}
glb_allobs_df$.lcn <- ""; glb_trnobs_df$.lcn <- "";
glb_allobs_df[glb_allobs_df[, glb_id_var] %in%
glb_fitobs_df[, glb_id_var], ".lcn"] <- "Fit"
glb_trnobs_df[glb_trnobs_df[, glb_id_var] %in%
glb_fitobs_df[, glb_id_var], ".lcn"] <- "Fit"
glb_allobs_df[glb_allobs_df[, glb_id_var] %in%
glb_OOBobs_df[, glb_id_var], ".lcn"] <- "OOB"
glb_trnobs_df[glb_trnobs_df[, glb_id_var] %in%
glb_OOBobs_df[, glb_id_var], ".lcn"] <- "OOB"
dsp_class_dstrb <- function(obs_df, location_var, partition_var) {
xtab_df <- mycreate_xtab_df(obs_df, c(location_var, partition_var))
rownames(xtab_df) <- xtab_df[, location_var]
xtab_df <- xtab_df[, -grepl(location_var, names(xtab_df))]
print(xtab_df)
print(xtab_df / rowSums(xtab_df, na.rm=TRUE))
}
# Ensure proper splits by glb_rsp_var_raw & user-specified feature for OOB vs. new
if (!is.null(glb_category_var)) {
if (glb_is_classification)
dsp_class_dstrb(glb_allobs_df, ".lcn", glb_rsp_var_raw)
newobs_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .src == "Test"),
glb_category_var)
names(newobs_ctgry_df) <-
gsub(".n", ".n.Tst", names(newobs_ctgry_df), fixed = TRUE)
OOBobs_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .lcn == "OOB"),
glb_category_var)
names(OOBobs_ctgry_df) <-
gsub(".n", ".n.OOB", names(OOBobs_ctgry_df), fixed = TRUE)
fitobs_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .lcn == "Fit"),
glb_category_var)
names(fitobs_ctgry_df) <-
gsub(".n", ".n.Fit", names(fitobs_ctgry_df), fixed = TRUE)
# glb_ctgry_df <- merge(newobs_ctgry_df, OOBobs_ctgry_df, by=glb_category_var
# , all=TRUE, suffixes=c(".Tst", ".OOB"))
glb_ctgry_df <- merge(fitobs_ctgry_df, OOBobs_ctgry_df, by = glb_category_var
, all = TRUE)
glb_ctgry_df <- merge(glb_ctgry_df, newobs_ctgry_df, by = glb_category_var
, all = TRUE)
glb_ctgry_df$.freqRatio.Fit <-
glb_ctgry_df$.n.Fit / sum(glb_ctgry_df$.n.Fit, na.rm = TRUE)
glb_ctgry_df$.freqRatio.OOB <-
glb_ctgry_df$.n.OOB / sum(glb_ctgry_df$.n.OOB, na.rm = TRUE)
glb_ctgry_df$.freqRatio.Tst <-
glb_ctgry_df$.n.Tst / sum(glb_ctgry_df$.n.Tst, na.rm = TRUE)
print(orderBy(~-.freqRatio.Tst-.freqRatio.OOB-.freqRatio.Fit, glb_ctgry_df))
}## sold.0 sold.1 sold.NA
## NA NA 798
## Fit 548 472 NA
## OOB 447 383 NA
## sold.0 sold.1 sold.NA
## NA NA 1
## Fit 0.5372549 0.4627451 NA
## OOB 0.5385542 0.4614458 NA
## prdl.my.fctr .n.Fit .n.OOB .n.Tst .freqRatio.Fit .freqRatio.OOB
## 3 iPad2 144 142 154 0.14117647 0.17108434
## 8 iPadmini 154 123 111 0.15098039 0.14819277
## 1 Unknown 108 96 92 0.10588235 0.11566265
## 2 iPad1 130 95 88 0.12745098 0.11445783
## 6 iPadAir 98 82 74 0.09607843 0.09879518
## 5 iPad4 84 73 68 0.08235294 0.08795181
## 7 iPadAir2 102 69 62 0.10000000 0.08313253
## 9 iPadmini2 54 53 56 0.05294118 0.06385542
## 4 iPad3 92 61 55 0.09019608 0.07349398
## 10 iPadmini3 54 36 38 0.05294118 0.04337349
## .freqRatio.Tst
## 3 0.19298246
## 8 0.13909774
## 1 0.11528822
## 2 0.11027569
## 6 0.09273183
## 5 0.08521303
## 7 0.07769424
## 9 0.07017544
## 4 0.06892231
## 10 0.04761905print("glb_allobs_df: "); print(dim(glb_allobs_df))## [1] "glb_allobs_df: "## [1] 2648 105print("glb_trnobs_df: "); print(dim(glb_trnobs_df))## [1] "glb_trnobs_df: "## [1] 1850 105print("glb_fitobs_df: "); print(dim(glb_fitobs_df))## [1] "glb_fitobs_df: "## [1] 1020 104print("glb_OOBobs_df: "); print(dim(glb_OOBobs_df))## [1] "glb_OOBobs_df: "## [1] 830 104print("glb_newobs_df: "); print(dim(glb_newobs_df))## [1] "glb_newobs_df: "## [1] 798 104# # Does not handle NULL or length(glb_id_var) > 1
if (glb_save_envir)
save(glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
file=paste0(glb_out_pfx, "blddfs_dsk.RData"))
# load(paste0(glb_out_pfx, "blddfs_dsk.RData"))
rm(split)## Warning in rm(split): object 'split' not foundglb_chunks_df <- myadd_chunk(glb_chunks_df, "select.features", major.inc=TRUE)## label step_major step_minor label_minor bgn
## 8 partition.data.training 4 0 0 299.110
## 9 select.features 5 0 0 300.026
## end elapsed
## 8 300.025 0.915
## 9 NA NAStep 5.0: select features
#stop(here"); glb_to_sav(); glb_allobs_df <- sav_allobs_df
print(glb_feats_df <- myselect_features(entity_df=glb_trnobs_df,
exclude_vars_as_features=glbFeatsExclude,
rsp_var=glb_rsp_var))## Warning in cor(data.matrix(entity_df[, sel_feats]), y =
## as.numeric(entity_df[, : the standard deviation is zero## id cor.y
## sold sold 1.000000000
## biddable biddable 0.549242842
## sprice.root2 sprice.root2 -0.511275385
## sprice.log10 sprice.log10 -0.469398937
## startprice startprice -0.458981936
## startprice.log10.predict startprice.log10.predict -0.408722051
## sprice.d20nexp sprice.d20nexp 0.397995133
## spdiff.cut.fctr spdiff.cut.fctr 0.290604966
## sprice.predict.diff sprice.predict.diff 0.271458109
## UniqueID UniqueID -0.190626150
## condition.fctr condition.fctr -0.155283820
## startprice.dgt1.is9 startprice.dgt1.is9 -0.138476687
## D.T.hous D.T.hous -0.129274878
## D.chrs.pnct05.n.log D.chrs.pnct05.n.log -0.120173065
## D.T.X100 D.T.X100 -0.111398502
## D.chrs.pnct06.n.log D.chrs.pnct06.n.log -0.095888068
## D.T.cosmet D.T.cosmet -0.088480863
## D.dgts.n.log D.dgts.n.log -0.082741631
## .clusterid .clusterid -0.080966421
## .clusterid.fctr .clusterid.fctr -0.080966421
## D.chrs.pnct14.n.log D.chrs.pnct14.n.log -0.075911474
## D.terms.post.stop.n D.terms.post.stop.n -0.075731613
## D.terms.post.stem.n D.terms.post.stem.n -0.074990216
## cellular.fctr cellular.fctr -0.073025939
## D.T.in. D.T.in. -0.071653919
## D.T.with D.T.with -0.066087601
## D.terms.post.stop.n.log D.terms.post.stop.n.log -0.062526435
## D.terms.post.stem.n.log D.terms.post.stem.n.log -0.062220631
## D.wrds.unq.n.log D.wrds.unq.n.log -0.062220631
## D.chrs.pnct09.n.log D.chrs.pnct09.n.log -0.061919863
## D.ratio.wrds.stop.n.wrds.n D.ratio.wrds.stop.n.wrds.n 0.059547328
## carrier.fctr carrier.fctr -0.058701911
## D.T.mint D.T.mint -0.055691207
## D.wrds.n.log D.wrds.n.log -0.055678363
## D.chrs.n.log D.chrs.n.log -0.055576665
## D.chrs.uppr.n.log D.chrs.uppr.n.log -0.054491610
## prdl.my.fctr prdl.my.fctr -0.054151937
## D.chrs.pnct28.n.log D.chrs.pnct28.n.log -0.052538378
## D.T.of D.T.of 0.052259530
## D.chrs.pnct12.n.log D.chrs.pnct12.n.log -0.049684867
## D.chrs.pnct15.n.log D.chrs.pnct15.n.log 0.048611857
## startprice.dcm2.is9 startprice.dcm2.is9 0.047716416
## D.weight.post.stem.sum D.weight.post.stem.sum -0.047105442
## D.weight.sum D.weight.sum -0.047105442
## D.weight.post.stop.sum D.weight.post.stop.sum -0.045009208
## color.fctr color.fctr -0.044231522
## D.terms.n.stem.stop.Ratio D.terms.n.stem.stop.Ratio 0.043562113
## startprice.dgt3.is9 startprice.dgt3.is9 -0.043150483
## D.T.minor D.T.minor -0.042692360
## D.T.screen D.T.screen 0.040605274
## startprice.dgt2.is9 startprice.dgt2.is9 -0.039616843
## D.chrs.pnct08.n.log D.chrs.pnct08.n.log -0.039606364
## D.T.condit D.T.condit -0.033248924
## D.T.light D.T.light -0.032814746
## D.chrs.pnct13.n.log D.chrs.pnct13.n.log -0.032095777
## D.T.from D.T.from -0.030653359
## D.chrs.pnct17.n.log D.chrs.pnct17.n.log 0.025087653
## D.chrs.pnct07.n.log D.chrs.pnct07.n.log 0.024330390
## D.chrs.pnct10.n.log D.chrs.pnct10.n.log -0.024107007
## D.T.or D.T.or -0.022614420
## D.chrs.pnct03.n.log D.chrs.pnct03.n.log -0.021557732
## D.chrs.pnct18.n.log D.chrs.pnct18.n.log -0.021557732
## D.P.gold D.P.gold -0.021557732
## D.chrs.pnct11.n.log D.chrs.pnct11.n.log -0.020186238
## D.P.white D.P.white 0.018597281
## D.T.good D.T.good 0.016394675
## D.chrs.pnct16.n.log D.chrs.pnct16.n.log 0.015642041
## storage.fctr storage.fctr -0.014549906
## D.P.air D.P.air -0.009203749
## startprice.dcm1.is9 startprice.dcm1.is9 -0.008364097
## D.P.mini D.P.mini -0.006894908
## D.chrs.pnct01.n.log D.chrs.pnct01.n.log 0.004291712
## D.wrds.stop.n.log D.wrds.stop.n.log 0.003851183
## D.P.spacegray D.P.spacegray 0.003532788
## D.T.use D.T.use 0.003025811
## D.weight.sum.stem.stop.Ratio D.weight.sum.stem.stop.Ratio 0.002381527
## .rnorm .rnorm -0.001273806
## D.P.black D.P.black -0.001181544
## D.ratio.weight.sum.wrds.n D.ratio.weight.sum.wrds.n -0.001080251
## D.chrs.pnct02.n.log D.chrs.pnct02.n.log NA
## D.chrs.pnct04.n.log D.chrs.pnct04.n.log NA
## D.chrs.pnct19.n.log D.chrs.pnct19.n.log NA
## D.chrs.pnct20.n.log D.chrs.pnct20.n.log NA
## D.chrs.pnct21.n.log D.chrs.pnct21.n.log NA
## D.chrs.pnct22.n.log D.chrs.pnct22.n.log NA
## D.chrs.pnct23.n.log D.chrs.pnct23.n.log NA
## D.chrs.pnct24.n.log D.chrs.pnct24.n.log NA
## D.chrs.pnct25.n.log D.chrs.pnct25.n.log NA
## D.chrs.pnct26.n.log D.chrs.pnct26.n.log NA
## D.chrs.pnct27.n.log D.chrs.pnct27.n.log NA
## D.chrs.pnct29.n.log D.chrs.pnct29.n.log NA
## D.chrs.pnct30.n.log D.chrs.pnct30.n.log NA
## D.P.http D.P.http NA
## exclude.as.feat cor.y.abs
## sold 1 1.000000000
## biddable 0 0.549242842
## sprice.root2 0 0.511275385
## sprice.log10 0 0.469398937
## startprice 1 0.458981936
## startprice.log10.predict 1 0.408722051
## sprice.d20nexp 0 0.397995133
## spdiff.cut.fctr 0 0.290604966
## sprice.predict.diff 1 0.271458109
## UniqueID 1 0.190626150
## condition.fctr 0 0.155283820
## startprice.dgt1.is9 0 0.138476687
## D.T.hous 0 0.129274878
## D.chrs.pnct05.n.log 0 0.120173065
## D.T.X100 0 0.111398502
## D.chrs.pnct06.n.log 0 0.095888068
## D.T.cosmet 0 0.088480863
## D.dgts.n.log 0 0.082741631
## .clusterid 1 0.080966421
## .clusterid.fctr 0 0.080966421
## D.chrs.pnct14.n.log 0 0.075911474
## D.terms.post.stop.n 1 0.075731613
## D.terms.post.stem.n 1 0.074990216
## cellular.fctr 0 0.073025939
## D.T.in. 0 0.071653919
## D.T.with 0 0.066087601
## D.terms.post.stop.n.log 0 0.062526435
## D.terms.post.stem.n.log 0 0.062220631
## D.wrds.unq.n.log 0 0.062220631
## D.chrs.pnct09.n.log 0 0.061919863
## D.ratio.wrds.stop.n.wrds.n 0 0.059547328
## carrier.fctr 0 0.058701911
## D.T.mint 0 0.055691207
## D.wrds.n.log 0 0.055678363
## D.chrs.n.log 0 0.055576665
## D.chrs.uppr.n.log 0 0.054491610
## prdl.my.fctr 0 0.054151937
## D.chrs.pnct28.n.log 0 0.052538378
## D.T.of 0 0.052259530
## D.chrs.pnct12.n.log 0 0.049684867
## D.chrs.pnct15.n.log 0 0.048611857
## startprice.dcm2.is9 0 0.047716416
## D.weight.post.stem.sum 0 0.047105442
## D.weight.sum 0 0.047105442
## D.weight.post.stop.sum 0 0.045009208
## color.fctr 0 0.044231522
## D.terms.n.stem.stop.Ratio 0 0.043562113
## startprice.dgt3.is9 0 0.043150483
## D.T.minor 0 0.042692360
## D.T.screen 0 0.040605274
## startprice.dgt2.is9 0 0.039616843
## D.chrs.pnct08.n.log 0 0.039606364
## D.T.condit 0 0.033248924
## D.T.light 0 0.032814746
## D.chrs.pnct13.n.log 0 0.032095777
## D.T.from 0 0.030653359
## D.chrs.pnct17.n.log 0 0.025087653
## D.chrs.pnct07.n.log 0 0.024330390
## D.chrs.pnct10.n.log 0 0.024107007
## D.T.or 0 0.022614420
## D.chrs.pnct03.n.log 0 0.021557732
## D.chrs.pnct18.n.log 0 0.021557732
## D.P.gold 0 0.021557732
## D.chrs.pnct11.n.log 0 0.020186238
## D.P.white 0 0.018597281
## D.T.good 0 0.016394675
## D.chrs.pnct16.n.log 0 0.015642041
## storage.fctr 0 0.014549906
## D.P.air 0 0.009203749
## startprice.dcm1.is9 0 0.008364097
## D.P.mini 0 0.006894908
## D.chrs.pnct01.n.log 0 0.004291712
## D.wrds.stop.n.log 0 0.003851183
## D.P.spacegray 0 0.003532788
## D.T.use 0 0.003025811
## D.weight.sum.stem.stop.Ratio 0 0.002381527
## .rnorm 0 0.001273806
## D.P.black 0 0.001181544
## D.ratio.weight.sum.wrds.n 0 0.001080251
## D.chrs.pnct02.n.log 0 NA
## D.chrs.pnct04.n.log 0 NA
## D.chrs.pnct19.n.log 0 NA
## D.chrs.pnct20.n.log 0 NA
## D.chrs.pnct21.n.log 0 NA
## D.chrs.pnct22.n.log 0 NA
## D.chrs.pnct23.n.log 0 NA
## D.chrs.pnct24.n.log 0 NA
## D.chrs.pnct25.n.log 0 NA
## D.chrs.pnct26.n.log 0 NA
## D.chrs.pnct27.n.log 0 NA
## D.chrs.pnct29.n.log 0 NA
## D.chrs.pnct30.n.log 0 NA
## D.P.http 0 NAprint(glb_feats_df <- orderBy(~-cor.y,
myfind_cor_features(feats_df=glb_feats_df, obs_df=glb_trnobs_df, rsp_var=glb_rsp_var,
nzv.freqCut=glb_nzv_freqCut, nzv.uniqueCut=glb_nzv_uniqueCut)))## [1] "cor(D.terms.post.stem.n.log, D.wrds.unq.n.log)=1.0000"
## [1] "cor(sold.fctr, D.terms.post.stem.n.log)=-0.0622"
## [1] "cor(sold.fctr, D.wrds.unq.n.log)=-0.0622"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.wrds.unq.n.log as highly correlated with
## D.terms.post.stem.n.log## [1] "cor(D.weight.post.stem.sum, D.weight.sum)=1.0000"
## [1] "cor(sold.fctr, D.weight.post.stem.sum)=-0.0471"
## [1] "cor(sold.fctr, D.weight.sum)=-0.0471"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.weight.sum as highly correlated with
## D.weight.post.stem.sum## [1] "cor(D.terms.post.stem.n.log, D.terms.post.stop.n.log)=0.9999"
## [1] "cor(sold.fctr, D.terms.post.stem.n.log)=-0.0622"
## [1] "cor(sold.fctr, D.terms.post.stop.n.log)=-0.0625"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.terms.post.stem.n.log as highly correlated
## with D.terms.post.stop.n.log## [1] "cor(D.chrs.n.log, D.chrs.uppr.n.log)=0.9996"
## [1] "cor(sold.fctr, D.chrs.n.log)=-0.0556"
## [1] "cor(sold.fctr, D.chrs.uppr.n.log)=-0.0545"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.chrs.uppr.n.log as highly correlated with
## D.chrs.n.log## [1] "cor(D.weight.post.stem.sum, D.weight.post.stop.sum)=0.9981"
## [1] "cor(sold.fctr, D.weight.post.stem.sum)=-0.0471"
## [1] "cor(sold.fctr, D.weight.post.stop.sum)=-0.0450"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.weight.post.stop.sum as highly correlated
## with D.weight.post.stem.sum## [1] "cor(D.terms.post.stop.n.log, D.wrds.n.log)=0.9956"
## [1] "cor(sold.fctr, D.terms.post.stop.n.log)=-0.0625"
## [1] "cor(sold.fctr, D.wrds.n.log)=-0.0557"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.wrds.n.log as highly correlated with
## D.terms.post.stop.n.log## [1] "cor(D.chrs.n.log, D.terms.post.stop.n.log)=0.9889"
## [1] "cor(sold.fctr, D.chrs.n.log)=-0.0556"
## [1] "cor(sold.fctr, D.terms.post.stop.n.log)=-0.0625"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.chrs.n.log as highly correlated with
## D.terms.post.stop.n.log## [1] "cor(D.ratio.wrds.stop.n.wrds.n, D.terms.post.stop.n.log)=-0.9745"
## [1] "cor(sold.fctr, D.ratio.wrds.stop.n.wrds.n)=0.0595"
## [1] "cor(sold.fctr, D.terms.post.stop.n.log)=-0.0625"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.ratio.wrds.stop.n.wrds.n as highly correlated
## with D.terms.post.stop.n.log## [1] "cor(sprice.d20nexp, sprice.log10)=-0.9316"
## [1] "cor(sold.fctr, sprice.d20nexp)=0.3980"
## [1] "cor(sold.fctr, sprice.log10)=-0.4694"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified sprice.d20nexp as highly correlated with
## sprice.log10## [1] "cor(D.terms.post.stop.n.log, D.weight.post.stem.sum)=0.9316"
## [1] "cor(sold.fctr, D.terms.post.stop.n.log)=-0.0625"
## [1] "cor(sold.fctr, D.weight.post.stem.sum)=-0.0471"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.weight.post.stem.sum as highly correlated
## with D.terms.post.stop.n.log## [1] "cor(D.T.X100, D.chrs.pnct05.n.log)=0.8813"
## [1] "cor(sold.fctr, D.T.X100)=-0.1114"
## [1] "cor(sold.fctr, D.chrs.pnct05.n.log)=-0.1202"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df
## = glb_trnobs_df, : Identified D.T.X100 as highly correlated with
## D.chrs.pnct05.n.log## [1] "cor(sprice.log10, sprice.root2)=0.8701"
## [1] "cor(sold.fctr, sprice.log10)=-0.4694"
## [1] "cor(sold.fctr, sprice.root2)=-0.5113"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified sprice.log10 as highly correlated with
## sprice.root2## [1] "cor(startprice.dcm1.is9, startprice.dcm2.is9)=0.8054"
## [1] "cor(sold.fctr, startprice.dcm1.is9)=-0.0084"
## [1] "cor(sold.fctr, startprice.dcm2.is9)=0.0477"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified startprice.dcm1.is9 as highly correlated with
## startprice.dcm2.is9## [1] "cor(D.chrs.pnct13.n.log, D.terms.post.stop.n.log)=0.7218"
## [1] "cor(sold.fctr, D.chrs.pnct13.n.log)=-0.0321"
## [1] "cor(sold.fctr, D.terms.post.stop.n.log)=-0.0625"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.chrs.pnct13.n.log as highly correlated with
## D.terms.post.stop.n.log## [1] "cor(carrier.fctr, cellular.fctr)=0.7131"
## [1] "cor(sold.fctr, carrier.fctr)=-0.0587"
## [1] "cor(sold.fctr, cellular.fctr)=-0.0730"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified carrier.fctr as highly correlated with
## cellular.fctr## id cor.y
## sold sold 1.000000000
## biddable biddable 0.549242842
## sprice.d20nexp sprice.d20nexp 0.397995133
## spdiff.cut.fctr spdiff.cut.fctr 0.290604966
## sprice.predict.diff sprice.predict.diff 0.271458109
## D.ratio.wrds.stop.n.wrds.n D.ratio.wrds.stop.n.wrds.n 0.059547328
## D.T.of D.T.of 0.052259530
## D.chrs.pnct15.n.log D.chrs.pnct15.n.log 0.048611857
## startprice.dcm2.is9 startprice.dcm2.is9 0.047716416
## D.terms.n.stem.stop.Ratio D.terms.n.stem.stop.Ratio 0.043562113
## D.T.screen D.T.screen 0.040605274
## D.chrs.pnct17.n.log D.chrs.pnct17.n.log 0.025087653
## D.chrs.pnct07.n.log D.chrs.pnct07.n.log 0.024330390
## D.P.white D.P.white 0.018597281
## D.T.good D.T.good 0.016394675
## D.chrs.pnct16.n.log D.chrs.pnct16.n.log 0.015642041
## D.chrs.pnct01.n.log D.chrs.pnct01.n.log 0.004291712
## D.wrds.stop.n.log D.wrds.stop.n.log 0.003851183
## D.P.spacegray D.P.spacegray 0.003532788
## D.T.use D.T.use 0.003025811
## D.weight.sum.stem.stop.Ratio D.weight.sum.stem.stop.Ratio 0.002381527
## D.ratio.weight.sum.wrds.n D.ratio.weight.sum.wrds.n -0.001080251
## D.P.black D.P.black -0.001181544
## .rnorm .rnorm -0.001273806
## D.P.mini D.P.mini -0.006894908
## startprice.dcm1.is9 startprice.dcm1.is9 -0.008364097
## D.P.air D.P.air -0.009203749
## storage.fctr storage.fctr -0.014549906
## D.chrs.pnct11.n.log D.chrs.pnct11.n.log -0.020186238
## D.P.gold D.P.gold -0.021557732
## D.chrs.pnct03.n.log D.chrs.pnct03.n.log -0.021557732
## D.chrs.pnct18.n.log D.chrs.pnct18.n.log -0.021557732
## D.T.or D.T.or -0.022614420
## D.chrs.pnct10.n.log D.chrs.pnct10.n.log -0.024107007
## D.T.from D.T.from -0.030653359
## D.chrs.pnct13.n.log D.chrs.pnct13.n.log -0.032095777
## D.T.light D.T.light -0.032814746
## D.T.condit D.T.condit -0.033248924
## D.chrs.pnct08.n.log D.chrs.pnct08.n.log -0.039606364
## startprice.dgt2.is9 startprice.dgt2.is9 -0.039616843
## D.T.minor D.T.minor -0.042692360
## startprice.dgt3.is9 startprice.dgt3.is9 -0.043150483
## color.fctr color.fctr -0.044231522
## D.weight.post.stop.sum D.weight.post.stop.sum -0.045009208
## D.weight.post.stem.sum D.weight.post.stem.sum -0.047105442
## D.weight.sum D.weight.sum -0.047105442
## D.chrs.pnct12.n.log D.chrs.pnct12.n.log -0.049684867
## D.chrs.pnct28.n.log D.chrs.pnct28.n.log -0.052538378
## prdl.my.fctr prdl.my.fctr -0.054151937
## D.chrs.uppr.n.log D.chrs.uppr.n.log -0.054491610
## D.chrs.n.log D.chrs.n.log -0.055576665
## D.wrds.n.log D.wrds.n.log -0.055678363
## D.T.mint D.T.mint -0.055691207
## carrier.fctr carrier.fctr -0.058701911
## D.chrs.pnct09.n.log D.chrs.pnct09.n.log -0.061919863
## D.terms.post.stem.n.log D.terms.post.stem.n.log -0.062220631
## D.wrds.unq.n.log D.wrds.unq.n.log -0.062220631
## D.terms.post.stop.n.log D.terms.post.stop.n.log -0.062526435
## D.T.with D.T.with -0.066087601
## D.T.in. D.T.in. -0.071653919
## cellular.fctr cellular.fctr -0.073025939
## D.terms.post.stem.n D.terms.post.stem.n -0.074990216
## D.terms.post.stop.n D.terms.post.stop.n -0.075731613
## D.chrs.pnct14.n.log D.chrs.pnct14.n.log -0.075911474
## .clusterid .clusterid -0.080966421
## .clusterid.fctr .clusterid.fctr -0.080966421
## D.dgts.n.log D.dgts.n.log -0.082741631
## D.T.cosmet D.T.cosmet -0.088480863
## D.chrs.pnct06.n.log D.chrs.pnct06.n.log -0.095888068
## D.T.X100 D.T.X100 -0.111398502
## D.chrs.pnct05.n.log D.chrs.pnct05.n.log -0.120173065
## D.T.hous D.T.hous -0.129274878
## startprice.dgt1.is9 startprice.dgt1.is9 -0.138476687
## condition.fctr condition.fctr -0.155283820
## UniqueID UniqueID -0.190626150
## startprice.log10.predict startprice.log10.predict -0.408722051
## startprice startprice -0.458981936
## sprice.log10 sprice.log10 -0.469398937
## sprice.root2 sprice.root2 -0.511275385
## D.P.http D.P.http NA
## D.chrs.pnct02.n.log D.chrs.pnct02.n.log NA
## D.chrs.pnct04.n.log D.chrs.pnct04.n.log NA
## D.chrs.pnct19.n.log D.chrs.pnct19.n.log NA
## D.chrs.pnct20.n.log D.chrs.pnct20.n.log NA
## D.chrs.pnct21.n.log D.chrs.pnct21.n.log NA
## D.chrs.pnct22.n.log D.chrs.pnct22.n.log NA
## D.chrs.pnct23.n.log D.chrs.pnct23.n.log NA
## D.chrs.pnct24.n.log D.chrs.pnct24.n.log NA
## D.chrs.pnct25.n.log D.chrs.pnct25.n.log NA
## D.chrs.pnct26.n.log D.chrs.pnct26.n.log NA
## D.chrs.pnct27.n.log D.chrs.pnct27.n.log NA
## D.chrs.pnct29.n.log D.chrs.pnct29.n.log NA
## D.chrs.pnct30.n.log D.chrs.pnct30.n.log NA
## exclude.as.feat cor.y.abs
## sold 1 1.000000000
## biddable 0 0.549242842
## sprice.d20nexp 0 0.397995133
## spdiff.cut.fctr 0 0.290604966
## sprice.predict.diff 1 0.271458109
## D.ratio.wrds.stop.n.wrds.n 0 0.059547328
## D.T.of 0 0.052259530
## D.chrs.pnct15.n.log 0 0.048611857
## startprice.dcm2.is9 0 0.047716416
## D.terms.n.stem.stop.Ratio 0 0.043562113
## D.T.screen 0 0.040605274
## D.chrs.pnct17.n.log 0 0.025087653
## D.chrs.pnct07.n.log 0 0.024330390
## D.P.white 0 0.018597281
## D.T.good 0 0.016394675
## D.chrs.pnct16.n.log 0 0.015642041
## D.chrs.pnct01.n.log 0 0.004291712
## D.wrds.stop.n.log 0 0.003851183
## D.P.spacegray 0 0.003532788
## D.T.use 0 0.003025811
## D.weight.sum.stem.stop.Ratio 0 0.002381527
## D.ratio.weight.sum.wrds.n 0 0.001080251
## D.P.black 0 0.001181544
## .rnorm 0 0.001273806
## D.P.mini 0 0.006894908
## startprice.dcm1.is9 0 0.008364097
## D.P.air 0 0.009203749
## storage.fctr 0 0.014549906
## D.chrs.pnct11.n.log 0 0.020186238
## D.P.gold 0 0.021557732
## D.chrs.pnct03.n.log 0 0.021557732
## D.chrs.pnct18.n.log 0 0.021557732
## D.T.or 0 0.022614420
## D.chrs.pnct10.n.log 0 0.024107007
## D.T.from 0 0.030653359
## D.chrs.pnct13.n.log 0 0.032095777
## D.T.light 0 0.032814746
## D.T.condit 0 0.033248924
## D.chrs.pnct08.n.log 0 0.039606364
## startprice.dgt2.is9 0 0.039616843
## D.T.minor 0 0.042692360
## startprice.dgt3.is9 0 0.043150483
## color.fctr 0 0.044231522
## D.weight.post.stop.sum 0 0.045009208
## D.weight.post.stem.sum 0 0.047105442
## D.weight.sum 0 0.047105442
## D.chrs.pnct12.n.log 0 0.049684867
## D.chrs.pnct28.n.log 0 0.052538378
## prdl.my.fctr 0 0.054151937
## D.chrs.uppr.n.log 0 0.054491610
## D.chrs.n.log 0 0.055576665
## D.wrds.n.log 0 0.055678363
## D.T.mint 0 0.055691207
## carrier.fctr 0 0.058701911
## D.chrs.pnct09.n.log 0 0.061919863
## D.terms.post.stem.n.log 0 0.062220631
## D.wrds.unq.n.log 0 0.062220631
## D.terms.post.stop.n.log 0 0.062526435
## D.T.with 0 0.066087601
## D.T.in. 0 0.071653919
## cellular.fctr 0 0.073025939
## D.terms.post.stem.n 1 0.074990216
## D.terms.post.stop.n 1 0.075731613
## D.chrs.pnct14.n.log 0 0.075911474
## .clusterid 1 0.080966421
## .clusterid.fctr 0 0.080966421
## D.dgts.n.log 0 0.082741631
## D.T.cosmet 0 0.088480863
## D.chrs.pnct06.n.log 0 0.095888068
## D.T.X100 0 0.111398502
## D.chrs.pnct05.n.log 0 0.120173065
## D.T.hous 0 0.129274878
## startprice.dgt1.is9 0 0.138476687
## condition.fctr 0 0.155283820
## UniqueID 1 0.190626150
## startprice.log10.predict 1 0.408722051
## startprice 1 0.458981936
## sprice.log10 0 0.469398937
## sprice.root2 0 0.511275385
## D.P.http 0 NA
## D.chrs.pnct02.n.log 0 NA
## D.chrs.pnct04.n.log 0 NA
## D.chrs.pnct19.n.log 0 NA
## D.chrs.pnct20.n.log 0 NA
## D.chrs.pnct21.n.log 0 NA
## D.chrs.pnct22.n.log 0 NA
## D.chrs.pnct23.n.log 0 NA
## D.chrs.pnct24.n.log 0 NA
## D.chrs.pnct25.n.log 0 NA
## D.chrs.pnct26.n.log 0 NA
## D.chrs.pnct27.n.log 0 NA
## D.chrs.pnct29.n.log 0 NA
## D.chrs.pnct30.n.log 0 NA
## cor.high.X freqRatio
## sold <NA> 1.163743
## biddable <NA> 1.215569
## sprice.d20nexp sprice.log10 2.807692
## spdiff.cut.fctr <NA> 1.668605
## sprice.predict.diff <NA> 1.000000
## D.ratio.wrds.stop.n.wrds.n D.terms.post.stop.n.log 18.067797
## D.T.of <NA> 145.000000
## D.chrs.pnct15.n.log <NA> 152.666667
## startprice.dcm2.is9 <NA> 2.195164
## D.terms.n.stem.stop.Ratio <NA> 85.142857
## D.T.screen <NA> 70.916667
## D.chrs.pnct17.n.log <NA> 1849.000000
## D.chrs.pnct07.n.log <NA> 107.529412
## D.P.white <NA> 230.125000
## D.T.good <NA> 95.166667
## D.chrs.pnct16.n.log <NA> 263.142857
## D.chrs.pnct01.n.log <NA> 52.705882
## D.wrds.stop.n.log <NA> 8.732484
## D.P.spacegray <NA> 461.500000
## D.T.use <NA> 86.631579
## D.weight.sum.stem.stop.Ratio <NA> 64.470588
## D.ratio.weight.sum.wrds.n <NA> 62.705882
## D.P.black <NA> 167.181818
## .rnorm <NA> 1.000000
## D.P.mini <NA> 96.315789
## startprice.dcm1.is9 startprice.dcm2.is9 1.616690
## D.P.air <NA> 122.266667
## storage.fctr <NA> 2.750742
## D.chrs.pnct11.n.log <NA> 9.382353
## D.P.gold <NA> 1849.000000
## D.chrs.pnct03.n.log <NA> 1849.000000
## D.chrs.pnct18.n.log <NA> 1849.000000
## D.T.or <NA> 87.950000
## D.chrs.pnct10.n.log <NA> 307.166667
## D.T.from <NA> 95.315789
## D.chrs.pnct13.n.log D.terms.post.stop.n.log 5.272374
## D.T.light <NA> 99.277778
## D.T.condit <NA> 36.829268
## D.chrs.pnct08.n.log <NA> 69.230769
## startprice.dgt2.is9 <NA> 6.905983
## D.T.minor <NA> 92.526316
## startprice.dgt3.is9 <NA> 461.500000
## color.fctr <NA> 1.592342
## D.weight.post.stop.sum D.weight.post.stem.sum 62.705882
## D.weight.post.stem.sum D.terms.post.stop.n.log 62.705882
## D.weight.sum D.weight.post.stem.sum 62.705882
## D.chrs.pnct12.n.log <NA> 28.467742
## D.chrs.pnct28.n.log <NA> 461.000000
## prdl.my.fctr <NA> 1.032491
## D.chrs.uppr.n.log D.chrs.n.log 15.661765
## D.chrs.n.log D.terms.post.stop.n.log 13.455696
## D.wrds.n.log D.terms.post.stop.n.log 11.430108
## D.T.mint <NA> 94.894737
## carrier.fctr cellular.fctr 3.184971
## D.chrs.pnct09.n.log <NA> 306.833333
## D.terms.post.stem.n.log D.terms.post.stop.n.log 11.714286
## D.wrds.unq.n.log D.terms.post.stem.n.log 11.714286
## D.terms.post.stop.n.log <NA> 13.325000
## D.T.with <NA> 46.888889
## D.T.in. <NA> 33.521739
## cellular.fctr <NA> 2.103053
## D.terms.post.stem.n <NA> 11.714286
## D.terms.post.stop.n <NA> 13.325000
## D.chrs.pnct14.n.log <NA> 35.176471
## .clusterid <NA> 10.959732
## .clusterid.fctr <NA> 10.959732
## D.dgts.n.log <NA> 34.340000
## D.T.cosmet <NA> 88.400000
## D.chrs.pnct06.n.log <NA> 60.466667
## D.T.X100 D.chrs.pnct05.n.log 180.500000
## D.chrs.pnct05.n.log <NA> 39.217391
## D.T.hous <NA> 81.681818
## startprice.dgt1.is9 <NA> 2.042763
## condition.fctr <NA> 4.010453
## UniqueID <NA> 1.000000
## startprice.log10.predict <NA> 1.230769
## startprice <NA> 2.807692
## sprice.log10 sprice.root2 2.807692
## sprice.root2 <NA> 2.807692
## D.P.http <NA> 0.000000
## D.chrs.pnct02.n.log <NA> 0.000000
## D.chrs.pnct04.n.log <NA> 0.000000
## D.chrs.pnct19.n.log <NA> 0.000000
## D.chrs.pnct20.n.log <NA> 0.000000
## D.chrs.pnct21.n.log <NA> 0.000000
## D.chrs.pnct22.n.log <NA> 0.000000
## D.chrs.pnct23.n.log <NA> 0.000000
## D.chrs.pnct24.n.log <NA> 0.000000
## D.chrs.pnct25.n.log <NA> 0.000000
## D.chrs.pnct26.n.log <NA> 0.000000
## D.chrs.pnct27.n.log <NA> 0.000000
## D.chrs.pnct29.n.log <NA> 0.000000
## D.chrs.pnct30.n.log <NA> 0.000000
## percentUnique zeroVar nzv is.cor.y.abs.low
## sold 0.10810811 FALSE FALSE FALSE
## biddable 0.10810811 FALSE FALSE FALSE
## sprice.d20nexp 30.21621622 FALSE FALSE FALSE
## spdiff.cut.fctr 0.43243243 FALSE FALSE FALSE
## sprice.predict.diff 96.10810811 FALSE FALSE FALSE
## D.ratio.wrds.stop.n.wrds.n 4.27027027 FALSE FALSE FALSE
## D.T.of 1.13513514 FALSE TRUE FALSE
## D.chrs.pnct15.n.log 0.16216216 FALSE TRUE FALSE
## startprice.dcm2.is9 0.10810811 FALSE FALSE FALSE
## D.terms.n.stem.stop.Ratio 0.64864865 FALSE TRUE FALSE
## D.T.screen 1.13513514 FALSE TRUE FALSE
## D.chrs.pnct17.n.log 0.10810811 FALSE TRUE FALSE
## D.chrs.pnct07.n.log 0.16216216 FALSE TRUE FALSE
## D.P.white 0.16216216 FALSE TRUE FALSE
## D.T.good 1.18918919 FALSE TRUE FALSE
## D.chrs.pnct16.n.log 0.16216216 FALSE TRUE FALSE
## D.chrs.pnct01.n.log 0.32432432 FALSE TRUE FALSE
## D.wrds.stop.n.log 0.54054054 FALSE FALSE FALSE
## D.P.spacegray 0.10810811 FALSE TRUE FALSE
## D.T.use 1.51351351 FALSE TRUE FALSE
## D.weight.sum.stem.stop.Ratio 33.56756757 FALSE FALSE FALSE
## D.ratio.weight.sum.wrds.n 34.81081081 FALSE FALSE TRUE
## D.P.black 0.10810811 FALSE TRUE TRUE
## .rnorm 100.00000000 FALSE FALSE FALSE
## D.P.mini 0.16216216 FALSE TRUE FALSE
## startprice.dcm1.is9 0.10810811 FALSE FALSE FALSE
## D.P.air 0.16216216 FALSE TRUE FALSE
## storage.fctr 0.27027027 FALSE FALSE FALSE
## D.chrs.pnct11.n.log 0.37837838 FALSE FALSE FALSE
## D.P.gold 0.10810811 FALSE TRUE FALSE
## D.chrs.pnct03.n.log 0.10810811 FALSE TRUE FALSE
## D.chrs.pnct18.n.log 0.10810811 FALSE TRUE FALSE
## D.T.or 1.08108108 FALSE TRUE FALSE
## D.chrs.pnct10.n.log 0.16216216 FALSE TRUE FALSE
## D.T.from 0.70270270 FALSE TRUE FALSE
## D.chrs.pnct13.n.log 0.48648649 FALSE FALSE FALSE
## D.T.light 0.91891892 FALSE TRUE FALSE
## D.T.condit 1.35135135 FALSE TRUE FALSE
## D.chrs.pnct08.n.log 0.21621622 FALSE TRUE FALSE
## startprice.dgt2.is9 0.10810811 FALSE FALSE FALSE
## D.T.minor 0.97297297 FALSE TRUE FALSE
## startprice.dgt3.is9 0.10810811 FALSE TRUE FALSE
## color.fctr 0.27027027 FALSE FALSE FALSE
## D.weight.post.stop.sum 34.75675676 FALSE FALSE FALSE
## D.weight.post.stem.sum 34.64864865 FALSE FALSE FALSE
## D.weight.sum 34.64864865 FALSE FALSE FALSE
## D.chrs.pnct12.n.log 0.21621622 FALSE TRUE FALSE
## D.chrs.pnct28.n.log 0.16216216 FALSE TRUE FALSE
## prdl.my.fctr 0.54054054 FALSE FALSE FALSE
## D.chrs.uppr.n.log 4.48648649 FALSE FALSE FALSE
## D.chrs.n.log 5.35135135 FALSE FALSE FALSE
## D.wrds.n.log 1.29729730 FALSE FALSE FALSE
## D.T.mint 1.02702703 FALSE TRUE FALSE
## carrier.fctr 0.37837838 FALSE FALSE FALSE
## D.chrs.pnct09.n.log 0.21621622 FALSE TRUE FALSE
## D.terms.post.stem.n.log 1.13513514 FALSE FALSE FALSE
## D.wrds.unq.n.log 1.13513514 FALSE FALSE FALSE
## D.terms.post.stop.n.log 1.13513514 FALSE FALSE FALSE
## D.T.with 1.02702703 FALSE TRUE FALSE
## D.T.in. 1.35135135 FALSE TRUE FALSE
## cellular.fctr 0.16216216 FALSE FALSE FALSE
## D.terms.post.stem.n 1.13513514 FALSE FALSE FALSE
## D.terms.post.stop.n 1.13513514 FALSE FALSE FALSE
## D.chrs.pnct14.n.log 0.27027027 FALSE TRUE FALSE
## .clusterid 0.16216216 FALSE FALSE FALSE
## .clusterid.fctr 0.16216216 FALSE FALSE FALSE
## D.dgts.n.log 0.59459459 FALSE TRUE FALSE
## D.T.cosmet 0.81081081 FALSE TRUE FALSE
## D.chrs.pnct06.n.log 0.16216216 FALSE TRUE FALSE
## D.T.X100 0.64864865 FALSE TRUE FALSE
## D.chrs.pnct05.n.log 0.10810811 FALSE TRUE FALSE
## D.T.hous 0.54054054 FALSE TRUE FALSE
## startprice.dgt1.is9 0.10810811 FALSE FALSE FALSE
## condition.fctr 0.32432432 FALSE FALSE FALSE
## UniqueID 100.00000000 FALSE FALSE FALSE
## startprice.log10.predict 87.94594595 FALSE FALSE FALSE
## startprice 30.21621622 FALSE FALSE FALSE
## sprice.log10 30.21621622 FALSE FALSE FALSE
## sprice.root2 30.21621622 FALSE FALSE FALSE
## D.P.http 0.05405405 TRUE TRUE NA
## D.chrs.pnct02.n.log 0.05405405 TRUE TRUE NA
## D.chrs.pnct04.n.log 0.05405405 TRUE TRUE NA
## D.chrs.pnct19.n.log 0.05405405 TRUE TRUE NA
## D.chrs.pnct20.n.log 0.05405405 TRUE TRUE NA
## D.chrs.pnct21.n.log 0.05405405 TRUE TRUE NA
## D.chrs.pnct22.n.log 0.05405405 TRUE TRUE NA
## D.chrs.pnct23.n.log 0.05405405 TRUE TRUE NA
## D.chrs.pnct24.n.log 0.05405405 TRUE TRUE NA
## D.chrs.pnct25.n.log 0.05405405 TRUE TRUE NA
## D.chrs.pnct26.n.log 0.05405405 TRUE TRUE NA
## D.chrs.pnct27.n.log 0.05405405 TRUE TRUE NA
## D.chrs.pnct29.n.log 0.05405405 TRUE TRUE NA
## D.chrs.pnct30.n.log 0.05405405 TRUE TRUE NAplt_feats_df <- glb_feats_df
print(myplot_scatter(plt_feats_df, "percentUnique", "freqRatio",
colorcol_name="nzv", jitter=TRUE) +
#geom_point(aes(shape=nzv)) +
geom_point() +
xlim(-5, 25) +
geom_hline(yintercept=glb_nzv_freqCut) +
geom_vline(xintercept=glb_nzv_uniqueCut))## Warning in myplot_scatter(plt_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "nzv", : converting nzv to class:factor## Warning: Removed 13 rows containing missing values (geom_point).## Warning: Removed 13 rows containing missing values (geom_point).## Warning: Removed 13 rows containing missing values (geom_point).
print(subset(glb_feats_df, nzv))## id cor.y
## D.T.of D.T.of 0.052259530
## D.chrs.pnct15.n.log D.chrs.pnct15.n.log 0.048611857
## D.terms.n.stem.stop.Ratio D.terms.n.stem.stop.Ratio 0.043562113
## D.T.screen D.T.screen 0.040605274
## D.chrs.pnct17.n.log D.chrs.pnct17.n.log 0.025087653
## D.chrs.pnct07.n.log D.chrs.pnct07.n.log 0.024330390
## D.P.white D.P.white 0.018597281
## D.T.good D.T.good 0.016394675
## D.chrs.pnct16.n.log D.chrs.pnct16.n.log 0.015642041
## D.chrs.pnct01.n.log D.chrs.pnct01.n.log 0.004291712
## D.P.spacegray D.P.spacegray 0.003532788
## D.T.use D.T.use 0.003025811
## D.P.black D.P.black -0.001181544
## D.P.mini D.P.mini -0.006894908
## D.P.air D.P.air -0.009203749
## D.P.gold D.P.gold -0.021557732
## D.chrs.pnct03.n.log D.chrs.pnct03.n.log -0.021557732
## D.chrs.pnct18.n.log D.chrs.pnct18.n.log -0.021557732
## D.T.or D.T.or -0.022614420
## D.chrs.pnct10.n.log D.chrs.pnct10.n.log -0.024107007
## D.T.from D.T.from -0.030653359
## D.T.light D.T.light -0.032814746
## D.T.condit D.T.condit -0.033248924
## D.chrs.pnct08.n.log D.chrs.pnct08.n.log -0.039606364
## D.T.minor D.T.minor -0.042692360
## startprice.dgt3.is9 startprice.dgt3.is9 -0.043150483
## D.chrs.pnct12.n.log D.chrs.pnct12.n.log -0.049684867
## D.chrs.pnct28.n.log D.chrs.pnct28.n.log -0.052538378
## D.T.mint D.T.mint -0.055691207
## D.chrs.pnct09.n.log D.chrs.pnct09.n.log -0.061919863
## D.T.with D.T.with -0.066087601
## D.T.in. D.T.in. -0.071653919
## D.chrs.pnct14.n.log D.chrs.pnct14.n.log -0.075911474
## D.dgts.n.log D.dgts.n.log -0.082741631
## D.T.cosmet D.T.cosmet -0.088480863
## D.chrs.pnct06.n.log D.chrs.pnct06.n.log -0.095888068
## D.T.X100 D.T.X100 -0.111398502
## D.chrs.pnct05.n.log D.chrs.pnct05.n.log -0.120173065
## D.T.hous D.T.hous -0.129274878
## D.P.http D.P.http NA
## D.chrs.pnct02.n.log D.chrs.pnct02.n.log NA
## D.chrs.pnct04.n.log D.chrs.pnct04.n.log NA
## D.chrs.pnct19.n.log D.chrs.pnct19.n.log NA
## D.chrs.pnct20.n.log D.chrs.pnct20.n.log NA
## D.chrs.pnct21.n.log D.chrs.pnct21.n.log NA
## D.chrs.pnct22.n.log D.chrs.pnct22.n.log NA
## D.chrs.pnct23.n.log D.chrs.pnct23.n.log NA
## D.chrs.pnct24.n.log D.chrs.pnct24.n.log NA
## D.chrs.pnct25.n.log D.chrs.pnct25.n.log NA
## D.chrs.pnct26.n.log D.chrs.pnct26.n.log NA
## D.chrs.pnct27.n.log D.chrs.pnct27.n.log NA
## D.chrs.pnct29.n.log D.chrs.pnct29.n.log NA
## D.chrs.pnct30.n.log D.chrs.pnct30.n.log NA
## exclude.as.feat cor.y.abs cor.high.X
## D.T.of 0 0.052259530 <NA>
## D.chrs.pnct15.n.log 0 0.048611857 <NA>
## D.terms.n.stem.stop.Ratio 0 0.043562113 <NA>
## D.T.screen 0 0.040605274 <NA>
## D.chrs.pnct17.n.log 0 0.025087653 <NA>
## D.chrs.pnct07.n.log 0 0.024330390 <NA>
## D.P.white 0 0.018597281 <NA>
## D.T.good 0 0.016394675 <NA>
## D.chrs.pnct16.n.log 0 0.015642041 <NA>
## D.chrs.pnct01.n.log 0 0.004291712 <NA>
## D.P.spacegray 0 0.003532788 <NA>
## D.T.use 0 0.003025811 <NA>
## D.P.black 0 0.001181544 <NA>
## D.P.mini 0 0.006894908 <NA>
## D.P.air 0 0.009203749 <NA>
## D.P.gold 0 0.021557732 <NA>
## D.chrs.pnct03.n.log 0 0.021557732 <NA>
## D.chrs.pnct18.n.log 0 0.021557732 <NA>
## D.T.or 0 0.022614420 <NA>
## D.chrs.pnct10.n.log 0 0.024107007 <NA>
## D.T.from 0 0.030653359 <NA>
## D.T.light 0 0.032814746 <NA>
## D.T.condit 0 0.033248924 <NA>
## D.chrs.pnct08.n.log 0 0.039606364 <NA>
## D.T.minor 0 0.042692360 <NA>
## startprice.dgt3.is9 0 0.043150483 <NA>
## D.chrs.pnct12.n.log 0 0.049684867 <NA>
## D.chrs.pnct28.n.log 0 0.052538378 <NA>
## D.T.mint 0 0.055691207 <NA>
## D.chrs.pnct09.n.log 0 0.061919863 <NA>
## D.T.with 0 0.066087601 <NA>
## D.T.in. 0 0.071653919 <NA>
## D.chrs.pnct14.n.log 0 0.075911474 <NA>
## D.dgts.n.log 0 0.082741631 <NA>
## D.T.cosmet 0 0.088480863 <NA>
## D.chrs.pnct06.n.log 0 0.095888068 <NA>
## D.T.X100 0 0.111398502 D.chrs.pnct05.n.log
## D.chrs.pnct05.n.log 0 0.120173065 <NA>
## D.T.hous 0 0.129274878 <NA>
## D.P.http 0 NA <NA>
## D.chrs.pnct02.n.log 0 NA <NA>
## D.chrs.pnct04.n.log 0 NA <NA>
## D.chrs.pnct19.n.log 0 NA <NA>
## D.chrs.pnct20.n.log 0 NA <NA>
## D.chrs.pnct21.n.log 0 NA <NA>
## D.chrs.pnct22.n.log 0 NA <NA>
## D.chrs.pnct23.n.log 0 NA <NA>
## D.chrs.pnct24.n.log 0 NA <NA>
## D.chrs.pnct25.n.log 0 NA <NA>
## D.chrs.pnct26.n.log 0 NA <NA>
## D.chrs.pnct27.n.log 0 NA <NA>
## D.chrs.pnct29.n.log 0 NA <NA>
## D.chrs.pnct30.n.log 0 NA <NA>
## freqRatio percentUnique zeroVar nzv
## D.T.of 145.00000 1.13513514 FALSE TRUE
## D.chrs.pnct15.n.log 152.66667 0.16216216 FALSE TRUE
## D.terms.n.stem.stop.Ratio 85.14286 0.64864865 FALSE TRUE
## D.T.screen 70.91667 1.13513514 FALSE TRUE
## D.chrs.pnct17.n.log 1849.00000 0.10810811 FALSE TRUE
## D.chrs.pnct07.n.log 107.52941 0.16216216 FALSE TRUE
## D.P.white 230.12500 0.16216216 FALSE TRUE
## D.T.good 95.16667 1.18918919 FALSE TRUE
## D.chrs.pnct16.n.log 263.14286 0.16216216 FALSE TRUE
## D.chrs.pnct01.n.log 52.70588 0.32432432 FALSE TRUE
## D.P.spacegray 461.50000 0.10810811 FALSE TRUE
## D.T.use 86.63158 1.51351351 FALSE TRUE
## D.P.black 167.18182 0.10810811 FALSE TRUE
## D.P.mini 96.31579 0.16216216 FALSE TRUE
## D.P.air 122.26667 0.16216216 FALSE TRUE
## D.P.gold 1849.00000 0.10810811 FALSE TRUE
## D.chrs.pnct03.n.log 1849.00000 0.10810811 FALSE TRUE
## D.chrs.pnct18.n.log 1849.00000 0.10810811 FALSE TRUE
## D.T.or 87.95000 1.08108108 FALSE TRUE
## D.chrs.pnct10.n.log 307.16667 0.16216216 FALSE TRUE
## D.T.from 95.31579 0.70270270 FALSE TRUE
## D.T.light 99.27778 0.91891892 FALSE TRUE
## D.T.condit 36.82927 1.35135135 FALSE TRUE
## D.chrs.pnct08.n.log 69.23077 0.21621622 FALSE TRUE
## D.T.minor 92.52632 0.97297297 FALSE TRUE
## startprice.dgt3.is9 461.50000 0.10810811 FALSE TRUE
## D.chrs.pnct12.n.log 28.46774 0.21621622 FALSE TRUE
## D.chrs.pnct28.n.log 461.00000 0.16216216 FALSE TRUE
## D.T.mint 94.89474 1.02702703 FALSE TRUE
## D.chrs.pnct09.n.log 306.83333 0.21621622 FALSE TRUE
## D.T.with 46.88889 1.02702703 FALSE TRUE
## D.T.in. 33.52174 1.35135135 FALSE TRUE
## D.chrs.pnct14.n.log 35.17647 0.27027027 FALSE TRUE
## D.dgts.n.log 34.34000 0.59459459 FALSE TRUE
## D.T.cosmet 88.40000 0.81081081 FALSE TRUE
## D.chrs.pnct06.n.log 60.46667 0.16216216 FALSE TRUE
## D.T.X100 180.50000 0.64864865 FALSE TRUE
## D.chrs.pnct05.n.log 39.21739 0.10810811 FALSE TRUE
## D.T.hous 81.68182 0.54054054 FALSE TRUE
## D.P.http 0.00000 0.05405405 TRUE TRUE
## D.chrs.pnct02.n.log 0.00000 0.05405405 TRUE TRUE
## D.chrs.pnct04.n.log 0.00000 0.05405405 TRUE TRUE
## D.chrs.pnct19.n.log 0.00000 0.05405405 TRUE TRUE
## D.chrs.pnct20.n.log 0.00000 0.05405405 TRUE TRUE
## D.chrs.pnct21.n.log 0.00000 0.05405405 TRUE TRUE
## D.chrs.pnct22.n.log 0.00000 0.05405405 TRUE TRUE
## D.chrs.pnct23.n.log 0.00000 0.05405405 TRUE TRUE
## D.chrs.pnct24.n.log 0.00000 0.05405405 TRUE TRUE
## D.chrs.pnct25.n.log 0.00000 0.05405405 TRUE TRUE
## D.chrs.pnct26.n.log 0.00000 0.05405405 TRUE TRUE
## D.chrs.pnct27.n.log 0.00000 0.05405405 TRUE TRUE
## D.chrs.pnct29.n.log 0.00000 0.05405405 TRUE TRUE
## D.chrs.pnct30.n.log 0.00000 0.05405405 TRUE TRUE
## is.cor.y.abs.low
## D.T.of FALSE
## D.chrs.pnct15.n.log FALSE
## D.terms.n.stem.stop.Ratio FALSE
## D.T.screen FALSE
## D.chrs.pnct17.n.log FALSE
## D.chrs.pnct07.n.log FALSE
## D.P.white FALSE
## D.T.good FALSE
## D.chrs.pnct16.n.log FALSE
## D.chrs.pnct01.n.log FALSE
## D.P.spacegray FALSE
## D.T.use FALSE
## D.P.black TRUE
## D.P.mini FALSE
## D.P.air FALSE
## D.P.gold FALSE
## D.chrs.pnct03.n.log FALSE
## D.chrs.pnct18.n.log FALSE
## D.T.or FALSE
## D.chrs.pnct10.n.log FALSE
## D.T.from FALSE
## D.T.light FALSE
## D.T.condit FALSE
## D.chrs.pnct08.n.log FALSE
## D.T.minor FALSE
## startprice.dgt3.is9 FALSE
## D.chrs.pnct12.n.log FALSE
## D.chrs.pnct28.n.log FALSE
## D.T.mint FALSE
## D.chrs.pnct09.n.log FALSE
## D.T.with FALSE
## D.T.in. FALSE
## D.chrs.pnct14.n.log FALSE
## D.dgts.n.log FALSE
## D.T.cosmet FALSE
## D.chrs.pnct06.n.log FALSE
## D.T.X100 FALSE
## D.chrs.pnct05.n.log FALSE
## D.T.hous FALSE
## D.P.http NA
## D.chrs.pnct02.n.log NA
## D.chrs.pnct04.n.log NA
## D.chrs.pnct19.n.log NA
## D.chrs.pnct20.n.log NA
## D.chrs.pnct21.n.log NA
## D.chrs.pnct22.n.log NA
## D.chrs.pnct23.n.log NA
## D.chrs.pnct24.n.log NA
## D.chrs.pnct25.n.log NA
## D.chrs.pnct26.n.log NA
## D.chrs.pnct27.n.log NA
## D.chrs.pnct29.n.log NA
## D.chrs.pnct30.n.log NAtmp_allobs_df <-
glb_allobs_df[, union(setdiff(names(glb_allobs_df), subset(glb_feats_df, nzv)$id),
glb_cluster_entropy_var)]
glb_trnobs_df <- subset(tmp_allobs_df, .src == "Train")
glb_newobs_df <- subset(tmp_allobs_df, .src == "Test")
glb_feats_df$interaction.feat <- NA
for (feat in names(glb_interaction_only_feats_lst))
glb_feats_df[glb_feats_df$id %in% feat, "interaction.feat"] <-
glb_interaction_only_feats_lst[[feat]]
#stop(here"); glb_to_sav(); glb_allobs_df <- sav_allobs_df
indep_vars <- subset(glb_feats_df, !nzv & (exclude.as.feat != 1))[, "id"]
numeric_indep_vars <- indep_vars[!grepl(".fctr", indep_vars, fixed=TRUE)]
glb_feats_df$shapiro.test.p.value <- NA
glb_feats_df[glb_feats_df$id %in% numeric_indep_vars, "shapiro.test.p.value"] <-
sapply(numeric_indep_vars, function(var) shapiro.test(glb_trnobs_df[, var])$p.value)
not_nrml_feats_df <- glb_feats_df %>%
subset(!is.na(shapiro.test.p.value)) %>%
subset((shapiro.test.p.value < 0.05) || (id == ".rnorm")) %>%
arrange(shapiro.test.p.value)
row.names(not_nrml_feats_df) <- not_nrml_feats_df$id
#plt_trnobs_df <- glb_trnobs_df[, c("D.npnct05.log", ".rnorm")]
plt_trnobs_df <- glb_trnobs_df[, c(union(not_nrml_feats_df$id[1:min(5, nrow(not_nrml_feats_df))],
".rnorm"), glb_cluster_entropy_var)]
print(myplot_violin(plt_trnobs_df, setdiff(names(plt_trnobs_df), glb_cluster_entropy_var),
xcol_name = glb_cluster_entropy_var) +
facet_wrap(~variable, scales="free"))
#myplot_histogram(plt_trnobs_df, "D.npnct11.log", fill_col_name="sold", show_stats = TRUE)
myadjust_interaction_feats <- function(vars_vctr) {
for (feat in subset(glb_feats_df, !is.na(interaction.feat))$id)
if (feat %in% vars_vctr)
vars_vctr <- union(setdiff(vars_vctr, feat),
paste0(glb_feats_df[glb_feats_df$id == feat, "interaction.feat"], ":",
feat))
return(vars_vctr)
}
myrun_rfe <- function(obs_df, indep_vars, sizes = NULL) {
rfe_obs_df <- myget_vectorized_obs_df(obs_df, glb_rsp_var, indep_vars)
predictors_vctr <- setdiff(names(rfe_obs_df), glb_rsp_var)
if (glb_is_regression) rfeFuncs <- lmFuncs else {
rfeFuncs <- ldaFuncs
# Delete non-variant columns
predictors_unqLen <- sapply(predictors_vctr, function(col)
length(unique(rfe_obs_df[, col])))
predictors_vctr <- predictors_vctr[predictors_unqLen > 1]
# Delete freqRatio >= 291
# plagiarized from caret:::nzv
predictors_freqRatio <-
apply(rfe_obs_df[, predictors_vctr], 2, function(data) {
t <- table(data[!is.na(data)])
if (length(t) <= 1) {
return(0)
}
w <- which.max(t)
return(max(t, na.rm = TRUE)/max(t[-w], na.rm = TRUE))
})
predictors_vctr <- predictors_vctr[predictors_freqRatio < 172]
}
if (is.null(sizes))
sizes <- tail(2 ^ (1:as.integer(log2(length(predictors_vctr)))), 5)
rfe_control <- rfeControl(functions = rfeFuncs, method = "repeatedcv",
number = glb_rcv_n_folds, repeats = glb_rcv_n_repeats,
verbose = TRUE, returnResamp = "all",
seeds = mygen_seeds(seeds_lst_len =
(glb_rcv_n_folds * glb_rcv_n_repeats) + 1,
seeds_elmnt_lst_len = (length(sizes) + 1))
, allowParallel = FALSE
)
set.seed(113)
rfe_results <- rfe(rfe_obs_df[, predictors_vctr],
rfe_obs_df[, glb_rsp_var],
sizes = sizes,
# metric = unlist(strsplit(glbMdlMetricsEval, "[.]"))[2],
# maximize = ifelse(unlist(strsplit(glbMdlMetricsEval, "[.]"))[1] == "max",
# TRUE, FALSE),
rfeControl = rfe_control)
print(rfe_results)
print(predictors(rfe_results))
# print(plot(rfe_results, type=c("g", "o")))
# print(plot(rfe_results))
print(ggplot(rfe_results))
return(rfe_results)
}
#stop(here"); glb_to_sav()
# shd .clusterid.fctr be excluded from this ? or include encoding of glb_category_var:.clusterid.fctr ?
indep_vars <- myadjust_interaction_feats(subset(glb_feats_df,
!nzv & (exclude.as.feat != 1))$id)
rfe_fit_results <- myrun_rfe(obs_df = glb_fitobs_df, indep_vars = indep_vars,
sizes = glbRFESizes[["RFE.X"]])## +(rfe) fit Fold1.Rep1 size: 70## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold1.Rep1 size: 70
## +(rfe) imp Fold1.Rep1
## -(rfe) imp Fold1.Rep1
## +(rfe) fit Fold1.Rep1 size: 69## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold1.Rep1 size: 69
## +(rfe) fit Fold1.Rep1 size: 68## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold1.Rep1 size: 68
## +(rfe) fit Fold1.Rep1 size: 67## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold1.Rep1 size: 67
## +(rfe) fit Fold1.Rep1 size: 64## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold1.Rep1 size: 64
## +(rfe) fit Fold1.Rep1 size: 32
## -(rfe) fit Fold1.Rep1 size: 32
## +(rfe) fit Fold1.Rep1 size: 16
## -(rfe) fit Fold1.Rep1 size: 16
## +(rfe) fit Fold1.Rep1 size: 8
## -(rfe) fit Fold1.Rep1 size: 8
## +(rfe) fit Fold1.Rep1 size: 4
## -(rfe) fit Fold1.Rep1 size: 4
## +(rfe) fit Fold2.Rep1 size: 70## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold2.Rep1 size: 70
## +(rfe) imp Fold2.Rep1
## -(rfe) imp Fold2.Rep1
## +(rfe) fit Fold2.Rep1 size: 69## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold2.Rep1 size: 69
## +(rfe) fit Fold2.Rep1 size: 68## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold2.Rep1 size: 68
## +(rfe) fit Fold2.Rep1 size: 67## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold2.Rep1 size: 67
## +(rfe) fit Fold2.Rep1 size: 64## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold2.Rep1 size: 64
## +(rfe) fit Fold2.Rep1 size: 32
## -(rfe) fit Fold2.Rep1 size: 32
## +(rfe) fit Fold2.Rep1 size: 16
## -(rfe) fit Fold2.Rep1 size: 16
## +(rfe) fit Fold2.Rep1 size: 8
## -(rfe) fit Fold2.Rep1 size: 8
## +(rfe) fit Fold2.Rep1 size: 4
## -(rfe) fit Fold2.Rep1 size: 4
## +(rfe) fit Fold3.Rep1 size: 70## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold3.Rep1 size: 70
## +(rfe) imp Fold3.Rep1
## -(rfe) imp Fold3.Rep1
## +(rfe) fit Fold3.Rep1 size: 69## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold3.Rep1 size: 69
## +(rfe) fit Fold3.Rep1 size: 68## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold3.Rep1 size: 68
## +(rfe) fit Fold3.Rep1 size: 67## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold3.Rep1 size: 67
## +(rfe) fit Fold3.Rep1 size: 64## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold3.Rep1 size: 64
## +(rfe) fit Fold3.Rep1 size: 32
## -(rfe) fit Fold3.Rep1 size: 32
## +(rfe) fit Fold3.Rep1 size: 16
## -(rfe) fit Fold3.Rep1 size: 16
## +(rfe) fit Fold3.Rep1 size: 8
## -(rfe) fit Fold3.Rep1 size: 8
## +(rfe) fit Fold3.Rep1 size: 4
## -(rfe) fit Fold3.Rep1 size: 4
## +(rfe) fit Fold1.Rep2 size: 70## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold1.Rep2 size: 70
## +(rfe) imp Fold1.Rep2
## -(rfe) imp Fold1.Rep2
## +(rfe) fit Fold1.Rep2 size: 69## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold1.Rep2 size: 69
## +(rfe) fit Fold1.Rep2 size: 68## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold1.Rep2 size: 68
## +(rfe) fit Fold1.Rep2 size: 67## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold1.Rep2 size: 67
## +(rfe) fit Fold1.Rep2 size: 64## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold1.Rep2 size: 64
## +(rfe) fit Fold1.Rep2 size: 32
## -(rfe) fit Fold1.Rep2 size: 32
## +(rfe) fit Fold1.Rep2 size: 16
## -(rfe) fit Fold1.Rep2 size: 16
## +(rfe) fit Fold1.Rep2 size: 8
## -(rfe) fit Fold1.Rep2 size: 8
## +(rfe) fit Fold1.Rep2 size: 4
## -(rfe) fit Fold1.Rep2 size: 4
## +(rfe) fit Fold2.Rep2 size: 70## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold2.Rep2 size: 70
## +(rfe) imp Fold2.Rep2
## -(rfe) imp Fold2.Rep2
## +(rfe) fit Fold2.Rep2 size: 69## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold2.Rep2 size: 69
## +(rfe) fit Fold2.Rep2 size: 68## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold2.Rep2 size: 68
## +(rfe) fit Fold2.Rep2 size: 67## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold2.Rep2 size: 67
## +(rfe) fit Fold2.Rep2 size: 64## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold2.Rep2 size: 64
## +(rfe) fit Fold2.Rep2 size: 32
## -(rfe) fit Fold2.Rep2 size: 32
## +(rfe) fit Fold2.Rep2 size: 16
## -(rfe) fit Fold2.Rep2 size: 16
## +(rfe) fit Fold2.Rep2 size: 8
## -(rfe) fit Fold2.Rep2 size: 8
## +(rfe) fit Fold2.Rep2 size: 4
## -(rfe) fit Fold2.Rep2 size: 4
## +(rfe) fit Fold3.Rep2 size: 70## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold3.Rep2 size: 70
## +(rfe) imp Fold3.Rep2
## -(rfe) imp Fold3.Rep2
## +(rfe) fit Fold3.Rep2 size: 69## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold3.Rep2 size: 69
## +(rfe) fit Fold3.Rep2 size: 68## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold3.Rep2 size: 68
## +(rfe) fit Fold3.Rep2 size: 67## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold3.Rep2 size: 67
## +(rfe) fit Fold3.Rep2 size: 64## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold3.Rep2 size: 64
## +(rfe) fit Fold3.Rep2 size: 32
## -(rfe) fit Fold3.Rep2 size: 32
## +(rfe) fit Fold3.Rep2 size: 16
## -(rfe) fit Fold3.Rep2 size: 16
## +(rfe) fit Fold3.Rep2 size: 8
## -(rfe) fit Fold3.Rep2 size: 8
## +(rfe) fit Fold3.Rep2 size: 4
## -(rfe) fit Fold3.Rep2 size: 4
## +(rfe) fit Fold1.Rep3 size: 70## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold1.Rep3 size: 70
## +(rfe) imp Fold1.Rep3
## -(rfe) imp Fold1.Rep3
## +(rfe) fit Fold1.Rep3 size: 69## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold1.Rep3 size: 69
## +(rfe) fit Fold1.Rep3 size: 68## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold1.Rep3 size: 68
## +(rfe) fit Fold1.Rep3 size: 67## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold1.Rep3 size: 67
## +(rfe) fit Fold1.Rep3 size: 64## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold1.Rep3 size: 64
## +(rfe) fit Fold1.Rep3 size: 32
## -(rfe) fit Fold1.Rep3 size: 32
## +(rfe) fit Fold1.Rep3 size: 16
## -(rfe) fit Fold1.Rep3 size: 16
## +(rfe) fit Fold1.Rep3 size: 8
## -(rfe) fit Fold1.Rep3 size: 8
## +(rfe) fit Fold1.Rep3 size: 4
## -(rfe) fit Fold1.Rep3 size: 4
## +(rfe) fit Fold2.Rep3 size: 70## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold2.Rep3 size: 70
## +(rfe) imp Fold2.Rep3
## -(rfe) imp Fold2.Rep3
## +(rfe) fit Fold2.Rep3 size: 69## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold2.Rep3 size: 69
## +(rfe) fit Fold2.Rep3 size: 68## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold2.Rep3 size: 68
## +(rfe) fit Fold2.Rep3 size: 67## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold2.Rep3 size: 67
## +(rfe) fit Fold2.Rep3 size: 64## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold2.Rep3 size: 64
## +(rfe) fit Fold2.Rep3 size: 32
## -(rfe) fit Fold2.Rep3 size: 32
## +(rfe) fit Fold2.Rep3 size: 16
## -(rfe) fit Fold2.Rep3 size: 16
## +(rfe) fit Fold2.Rep3 size: 8
## -(rfe) fit Fold2.Rep3 size: 8
## +(rfe) fit Fold2.Rep3 size: 4
## -(rfe) fit Fold2.Rep3 size: 4
## +(rfe) fit Fold3.Rep3 size: 70## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold3.Rep3 size: 70
## +(rfe) imp Fold3.Rep3
## -(rfe) imp Fold3.Rep3
## +(rfe) fit Fold3.Rep3 size: 69## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold3.Rep3 size: 69
## +(rfe) fit Fold3.Rep3 size: 68## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold3.Rep3 size: 68
## +(rfe) fit Fold3.Rep3 size: 67## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold3.Rep3 size: 67
## +(rfe) fit Fold3.Rep3 size: 64## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold3.Rep3 size: 64
## +(rfe) fit Fold3.Rep3 size: 32
## -(rfe) fit Fold3.Rep3 size: 32
## +(rfe) fit Fold3.Rep3 size: 16
## -(rfe) fit Fold3.Rep3 size: 16
## +(rfe) fit Fold3.Rep3 size: 8
## -(rfe) fit Fold3.Rep3 size: 8
## +(rfe) fit Fold3.Rep3 size: 4
## -(rfe) fit Fold3.Rep3 size: 4## Warning in lda.default(x, grouping, ...): variables are collinear##
## Recursive feature selection
##
## Outer resampling method: Cross-Validated (3 fold, repeated 3 times)
##
## Resampling performance over subset size:
##
## Variables Accuracy Kappa AccuracySD KappaSD Selected
## 4 0.7951 0.5866 0.01779 0.03553
## 8 0.7954 0.5870 0.02906 0.05706
## 16 0.8046 0.6049 0.02248 0.04531
## 32 0.8118 0.6190 0.02555 0.05141
## 64 0.8183 0.6330 0.02513 0.05031
## 67 0.8203 0.6372 0.02865 0.05763
## 68 0.8189 0.6347 0.02863 0.05748
## 69 0.8258 0.6482 0.02137 0.04275 *
## 70 0.8258 0.6482 0.02137 0.04275
##
## The top 5 variables (out of 69):
## sprice.d20nexp, sprice.log10, sprice.root2, biddable, spdiff.cut.fctr(1,10]
##
## [1] "sprice.d20nexp"
## [2] "sprice.log10"
## [3] "sprice.root2"
## [4] "biddable"
## [5] "spdiff.cut.fctr(1,10]"
## [6] "spdiff.cut.fctr(-1,0]"
## [7] "spdiff.cut.fctr(10,100]"
## [8] "cellular.fctr0:carrier.fctrNone"
## [9] "spdiff.cut.fctr(-10,-1]"
## [10] "prdl.my.fctriPad1"
## [11] "spdiff.cut.fctr(0,1]"
## [12] "D.ratio.wrds.stop.n.wrds.n"
## [13] "storage.fctr16"
## [14] "condition.fctrFor parts or not working"
## [15] "D.weight.sum.stem.stop.Ratio"
## [16] "color.fctrUnknown"
## [17] "prdl.my.fctriPad3"
## [18] "startprice.dcm2.is9"
## [19] "prdl.my.fctriPad2"
## [20] ".rnorm"
## [21] "storage.fctr32"
## [22] "prdl.my.fctriPadmini"
## [23] "cellular.fctr1:carrier.fctrVerizon"
## [24] "D.chrs.pnct11.n.log"
## [25] "prdl.my.fctriPad1:.clusterid.fctr2"
## [26] "prdl.my.fctriPadAir:.clusterid.fctr2"
## [27] "prdl.my.fctriPad1:.clusterid.fctr3"
## [28] "prdl.my.fctriPadmini2"
## [29] "prdl.my.fctriPad4:.clusterid.fctr2"
## [30] "prdl.my.fctriPad3:.clusterid.fctr2"
## [31] "cellular.fctr1:carrier.fctrSprint"
## [32] "storage.fctr64"
## [33] "prdl.my.fctrUnknown:.clusterid.fctr3"
## [34] "cellular.fctr1:carrier.fctrT-Mobile"
## [35] "D.wrds.stop.n.log"
## [36] "prdl.my.fctrUnknown:.clusterid.fctr2"
## [37] "condition.fctrManufacturer refurbished"
## [38] "startprice.dcm1.is9"
## [39] "condition.fctrNew other (see details)"
## [40] "prdl.my.fctriPadAir"
## [41] "prdl.my.fctriPad4"
## [42] "color.fctrGold"
## [43] "prdl.my.fctriPad4:.clusterid.fctr3"
## [44] "cellular.fctr1:carrier.fctrUnknown"
## [45] "startprice.dgt2.is9"
## [46] "color.fctrWhite"
## [47] "prdl.my.fctriPadAir2"
## [48] "D.ratio.weight.sum.wrds.n"
## [49] "storage.fctrUnknown"
## [50] "spdiff.cut.fctr(100,1e+03]"
## [51] "color.fctrSpace Gray"
## [52] "D.chrs.pnct13.n.log"
## [53] "prdl.my.fctriPadmini3"
## [54] "cellular.fctrUnknown"
## [55] "cellular.fctrUnknown:carrier.fctrUnknown"
## [56] "cellular.fctr1"
## [57] "condition.fctrSeller refurbished"
## [58] "D.weight.post.stop.sum"
## [59] "D.weight.post.stem.sum"
## [60] "D.weight.sum"
## [61] "D.wrds.n.log"
## [62] "D.chrs.uppr.n.log"
## [63] "D.chrs.n.log"
## [64] "D.wrds.unq.n.log"
## [65] "D.terms.post.stem.n.log"
## [66] "D.terms.post.stop.n.log"
## [67] "condition.fctrNew"
## [68] "startprice.dgt1.is9"
## [69] "spdiff.cut.fctr(-100,-10]"
# print(all.equal(rfe_results[-which(names(rfe_results) == "times")],
# sav_rfe_results[-which(names(sav_rfe_results) == "times")]))
# require(mRMRe)
# indep_vars_vctr <- subset(glb_feats_df, !nzv &
# (exclude.as.feat != 1))[, "id"]
# indep_vars_vctr <- setdiff(indep_vars_vctr,
# myfind_fctr_cols_df(glb_trnobs_df[, c(glb_rsp_var, indep_vars_vctr)]))
# tmp_trnobs_df <- glb_trnobs_df[, c(glb_rsp_var, indep_vars_vctr)]
# tmp_trnobs_df$biddable <- as.numeric(tmp_trnobs_df$biddable)
# dd <- mRMR.data(data = tmp_trnobs_df)
# mRMRe.fltr <- mRMR.classic(data = dd, target_indices = c(1), feature_count = 10)
# print(solutions(mRMRe.fltr)[[1]])
# print(apply(solutions(mRMRe.fltr)[[1]], 2, function(x, y) { return(y[x]) },
# y=featureNames(dd)))
# print(featureNames(dd)[solutions(mRMRe.fltr)[[1]]])
# print(mRMRe.fltr@filters); print(mRMRe.fltr@scores)
mycheck_problem_data(glb_allobs_df, featsExclude = glbFeatsExclude,
fctrMaxUniqVals = glbFctrMaxUniqVals, terminate = TRUE)## [1] "numeric data missing in : "
## sold sold.fctr
## 798 798
## [1] "numeric data w/ 0s in : "
## biddable sold
## 1437 995
## sprice.log10 cellular.fctr
## 31 1590
## D.terms.post.stop.n D.terms.post.stop.n.log
## 1516 1516
## D.weight.post.stop.sum D.terms.post.stem.n
## 1516 1516
## D.terms.post.stem.n.log D.weight.post.stem.sum
## 1516 1516
## D.T.condit D.T.use
## 2156 2363
## D.T.in. D.T.good
## 2216 2451
## D.T.screen D.T.with
## 2440 2434
## D.T.of D.T.mint
## 2497 2585
## D.T.or D.T.cosmet
## 2523 2531
## D.T.minor D.T.light
## 2531 2567
## D.T.X100 D.T.from
## 2584 2591
## D.T.hous D.wrds.n.log
## 2576 1513
## D.wrds.unq.n.log D.weight.sum
## 1516 1516
## D.ratio.weight.sum.wrds.n D.chrs.n.log
## 1516 1513
## D.chrs.uppr.n.log D.dgts.n.log
## 1515 2452
## D.chrs.pnct01.n.log D.chrs.pnct02.n.log
## 2570 2648
## D.chrs.pnct03.n.log D.chrs.pnct04.n.log
## 2647 2648
## D.chrs.pnct05.n.log D.chrs.pnct06.n.log
## 2582 2596
## D.chrs.pnct07.n.log D.chrs.pnct08.n.log
## 2611 2572
## D.chrs.pnct09.n.log D.chrs.pnct10.n.log
## 2632 2639
## D.chrs.pnct11.n.log D.chrs.pnct12.n.log
## 2294 2534
## D.chrs.pnct13.n.log D.chrs.pnct14.n.log
## 1930 2574
## D.chrs.pnct15.n.log D.chrs.pnct16.n.log
## 2628 2639
## D.chrs.pnct17.n.log D.chrs.pnct18.n.log
## 2646 2647
## D.chrs.pnct19.n.log D.chrs.pnct20.n.log
## 2648 2648
## D.chrs.pnct21.n.log D.chrs.pnct22.n.log
## 2648 2648
## D.chrs.pnct23.n.log D.chrs.pnct24.n.log
## 2648 2648
## D.chrs.pnct25.n.log D.chrs.pnct26.n.log
## 2648 2648
## D.chrs.pnct27.n.log D.chrs.pnct28.n.log
## 2648 2640
## D.chrs.pnct29.n.log D.chrs.pnct30.n.log
## 2648 2648
## D.wrds.stop.n.log D.P.http
## 1965 2648
## D.P.mini D.P.air
## 2615 2627
## D.P.black D.P.white
## 2631 2638
## D.P.gold D.P.spacegray
## 2647 2642
## startprice.dgt1.is9 startprice.dgt2.is9
## 1779 2290
## startprice.dgt3.is9 startprice.dcm1.is9
## 2643 1653
## startprice.dcm2.is9
## 1826
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 1513 0 0 0 0 0
## productline .grpid descr.my .lcn
## 0 NA 1513 798# glb_allobs_df %>% filter(is.na(Married.fctr)) %>% tbl_df()
# glb_allobs_df %>% count(Married.fctr)
# levels(glb_allobs_df$Married.fctr)
print("glb_feats_df:"); print(dim(glb_feats_df))## [1] "glb_feats_df:"## [1] 93 12sav_feats_df <- glb_feats_df
glb_feats_df <- sav_feats_df
glb_feats_df[, "rsp_var_raw"] <- FALSE
glb_feats_df[glb_feats_df$id == glb_rsp_var_raw, "rsp_var_raw"] <- TRUE
glb_feats_df$exclude.as.feat <- (glb_feats_df$exclude.as.feat == 1)
if (!is.null(glb_id_var) && glb_id_var != ".rownames")
glb_feats_df[glb_feats_df$id %in% glb_id_var, "id_var"] <- TRUE
add_feats_df <- data.frame(id=glb_rsp_var, exclude.as.feat=TRUE, rsp_var=TRUE)
row.names(add_feats_df) <- add_feats_df$id; print(add_feats_df)## id exclude.as.feat rsp_var
## sold.fctr sold.fctr TRUE TRUEglb_feats_df <- myrbind_df(glb_feats_df, add_feats_df)
if (glb_id_var != ".rownames")
print(subset(glb_feats_df, rsp_var_raw | rsp_var | id_var)) else
print(subset(glb_feats_df, rsp_var_raw | rsp_var)) ## id cor.y exclude.as.feat cor.y.abs cor.high.X
## sold sold 1.0000000 TRUE 1.0000000 <NA>
## UniqueID UniqueID -0.1906261 TRUE 0.1906261 <NA>
## sold.fctr sold.fctr NA TRUE NA <NA>
## freqRatio percentUnique zeroVar nzv is.cor.y.abs.low
## sold 1.163743 0.1081081 FALSE FALSE FALSE
## UniqueID 1.000000 100.0000000 FALSE FALSE FALSE
## sold.fctr NA NA NA NA NA
## interaction.feat shapiro.test.p.value rsp_var_raw id_var rsp_var
## sold <NA> NA TRUE NA NA
## UniqueID <NA> NA FALSE TRUE NA
## sold.fctr <NA> NA NA NA TRUEprint("glb_feats_df vs. glb_allobs_df: "); ## [1] "glb_feats_df vs. glb_allobs_df: "print(setdiff(glb_feats_df$id, names(glb_allobs_df)))## character(0)print("glb_allobs_df vs. glb_feats_df: "); ## [1] "glb_allobs_df vs. glb_feats_df: "# Ensure these are only chr vars
print(setdiff(setdiff(names(glb_allobs_df), glb_feats_df$id),
myfind_chr_cols_df(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df,
glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
file=paste0(glb_out_pfx, "selfts_dsk.RData"))
# load(paste0(glb_out_pfx, "blddfs_dsk.RData"))
# if (!all.equal(tmp_feats_df, glb_feats_df))
# stop("glb_feats_df r/w not working")
# if (!all.equal(tmp_entity_df, glb_allobs_df))
# stop("glb_allobs_df r/w not working")
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=TRUE)## label step_major step_minor label_minor bgn end
## 9 select.features 5 0 0 300.026 311.875
## 10 fit.models 6 0 0 311.876 NA
## elapsed
## 9 11.849
## 10 NAStep 6.0: fit models
# load(paste0(glb_out_pfx, "dsk.RData"))
get_model_sel_frmla <- function() {
model_evl_terms <- c(NULL)
# min.aic.fit might not be avl
lclMdlEvlCriteria <-
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)]
for (metric in lclMdlEvlCriteria)
model_evl_terms <- c(model_evl_terms,
ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
if (glb_is_classification && glb_is_binomial)
model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse = " "))
return(model_sel_frmla)
}
get_dsp_models_df <- function() {
dsp_models_cols <- c("id",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
dsp_models_df <-
#orderBy(get_model_sel_frmla(), glb_models_df)[, c("id", glbMdlMetricsEval)]
orderBy(get_model_sel_frmla(), glb_models_df)[, dsp_models_cols]
nCvMdl <- sapply(glb_models_lst, function(mdl) nrow(mdl$results))
nParams <- sapply(glb_models_lst, function(mdl) ifelse(mdl$method == "custom", 0,
nrow(subset(modelLookup(mdl$method), parameter != "parameter"))))
# nCvMdl <- nCvMdl[names(nCvMdl) != "avNNet"]
# nParams <- nParams[names(nParams) != "avNNet"]
if (length(cvMdlProblems <- nCvMdl[nCvMdl <= nParams]) > 0) {
print("Cross Validation issues:")
warning("Cross Validation issues:")
print(cvMdlProblems)
}
pltMdls <- setdiff(names(nCvMdl), names(cvMdlProblems))
pltMdls <- setdiff(pltMdls, names(nParams[nParams == 0]))
# length(pltMdls) == 21
png(paste0(glb_out_pfx, "bestTune.png"), width = 480 * 2, height = 480 * 4)
grid.newpage()
pushViewport(viewport(layout = grid.layout(ceiling(length(pltMdls) / 2.0), 2)))
pltIx <- 1
for (mdlId in pltMdls) {
print(ggplot(glb_models_lst[[mdlId]], highBestTune = TRUE) + labs(title = mdlId),
vp = viewport(layout.pos.row = ceiling(pltIx / 2.0),
layout.pos.col = ((pltIx - 1) %% 2) + 1))
pltIx <- pltIx + 1
}
dev.off()
return(dsp_models_df)
}
#get_dsp_models_df()
if (glb_is_classification && glb_is_binomial &&
(length(unique(glb_fitobs_df[, glb_rsp_var])) < 2))
stop("glb_fitobs_df$", glb_rsp_var, ": contains less than 2 unique values: ",
paste0(unique(glb_fitobs_df[, glb_rsp_var]), collapse=", "))
max_cor_y_x_vars <- orderBy(~ -cor.y.abs,
subset(glb_feats_df, (exclude.as.feat == 0) & !nzv & !is.cor.y.abs.low &
is.na(cor.high.X)))[1:2, "id"]
# while(length(max_cor_y_x_vars) < 2) {
# max_cor_y_x_vars <- c(max_cor_y_x_vars, orderBy(~ -cor.y.abs,
# subset(glb_feats_df, (exclude.as.feat == 0) & !is.cor.y.abs.low))[3, "id"])
# }
#stop(here"); glb_to_sav(); glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df
if (!is.null(glb_Baseline_mdl_var)) {
if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) &
(glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] >
glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var,
" than the Baseline var: ", glb_Baseline_mdl_var)
}
glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
# Model specs
c("id.prefix", "method", "type",
# trainControl params
"preProc.method", "cv.n.folds", "cv.n.repeats", "summary.fn",
# train params
"metric", "metric.maximize", "tune.df")## [1] "id.prefix" "method" "type"
## [4] "preProc.method" "cv.n.folds" "cv.n.repeats"
## [7] "summary.fn" "metric" "metric.maximize"
## [10] "tune.df"# Baseline
if (!is.null(glb_Baseline_mdl_var))
ret_lst <- myfit_mdl(mdl_id="Baseline",
model_method="mybaseln_classfr",
indep_vars_vctr=glb_Baseline_mdl_var,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
# Most Frequent Outcome "MFO" model: mean(y) for regression
# Not using caret's nullModel since model stats not avl
# Cannot use rpart for multinomial classification since it predicts non-MFO
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "MFO", type = glb_model_type, trainControl.method = "none",
train.method = ifelse(glb_is_regression, "lm", "myMFO_classfr"))),
indep_vars = ".rnorm", rsp_var = glb_rsp_var,
fit_df = glb_fitobs_df, OOB_df = glb_OOBobs_df)## [1] "fitting model: MFO.myMFO_classfr"
## [1] " indep_vars: .rnorm"## Warning in if (mdl_specs_lst[["train.method"]] %in% c("bayesglm", "glm", :
## the condition has length > 1 and only the first element will be used## Fitting parameter = none on full training set
## [1] "in MFO.Classifier$fit"
## [1] "unique.vals:"
## [1] N Y
## Levels: N Y
## [1] "unique.prob:"
## y
## N Y
## 0.5372549 0.4627451
## [1] "MFO.val:"
## [1] "N"
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 -none- numeric
## MFO.val 1 -none- character
## x.names 1 -none- character
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"## Loading required package: ROCR
## Loading required package: gplots
##
## Attaching package: 'gplots'
##
## The following object is masked from 'package:wordcloud':
##
## textplot
##
## The following object is masked from 'package:stats':
##
## lowess## [1] "in MFO.Classifier$prob"
## N Y
## 1 0.5372549 0.4627451
## 2 0.5372549 0.4627451
## 3 0.5372549 0.4627451
## 4 0.5372549 0.4627451
## 5 0.5372549 0.4627451
## 6 0.5372549 0.4627451
## sold.fctr sold.fctr.predict.MFO.myMFO_classfr.Y
## 1 N 548
## 2 Y 472
## Prediction
## Reference N Y
## N 0 548
## Y 0 472
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 4.627451e-01 0.000000e+00 4.318011e-01 4.939047e-01 5.372549e-01
## AccuracyPValue McnemarPValue
## 9.999992e-01 9.327417e-121
## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"
## [1] "in MFO.Classifier$prob"
## N Y
## 1 0.5372549 0.4627451
## 2 0.5372549 0.4627451
## 3 0.5372549 0.4627451
## 4 0.5372549 0.4627451
## 5 0.5372549 0.4627451
## 6 0.5372549 0.4627451
## sold.fctr sold.fctr.predict.MFO.myMFO_classfr.Y
## 1 N 447
## 2 Y 383
## Prediction
## Reference N Y
## N 0 447
## Y 0 383
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 4.614458e-01 0.000000e+00 4.271177e-01 4.960486e-01 5.385542e-01
## AccuracyPValue McnemarPValue
## 9.999963e-01 8.826336e-99
## id feats max.nTuningRuns min.elapsedtime.everything
## 1 MFO.myMFO_classfr .rnorm 0 0.289
## min.elapsedtime.final max.AUCpROC.fit max.Sens.fit max.Spec.fit
## 1 0.002 0.5 1 0
## max.AUCROCR.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.4 0.6327078 0.4627451
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.4318011 0.4939047 0
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.5 1 0 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.4 0.6314922 0.4614458
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.4271177 0.4960486 0if (glb_is_classification)
# "random" model - only for classification;
# none needed for regression since it is same as MFO
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Random", type = glb_model_type, trainControl.method = "none",
train.method = "myrandom_classfr")),
indep_vars = ".rnorm", rsp_var = glb_rsp_var,
fit_df = glb_fitobs_df, OOB_df = glb_OOBobs_df)## [1] "fitting model: Random.myrandom_classfr"
## [1] " indep_vars: .rnorm"## Warning in if (mdl_specs_lst[["train.method"]] %in% c("bayesglm", "glm", :
## the condition has length > 1 and only the first element will be used## Fitting parameter = none on full training set
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 table numeric
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] "in Random.Classifier$prob"
## sold.fctr sold.fctr.predict.Random.myrandom_classfr.Y
## 1 N 548
## 2 Y 472
## Prediction
## Reference N Y
## N 0 548
## Y 0 472
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 4.627451e-01 0.000000e+00 4.318011e-01 4.939047e-01 5.372549e-01
## AccuracyPValue McnemarPValue
## 9.999992e-01 9.327417e-121
## [1] "in Random.Classifier$prob"
## sold.fctr sold.fctr.predict.Random.myrandom_classfr.Y
## 1 N 447
## 2 Y 383
## Prediction
## Reference N Y
## N 0 447
## Y 0 383
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 4.614458e-01 0.000000e+00 4.271177e-01 4.960486e-01 5.385542e-01
## AccuracyPValue McnemarPValue
## 9.999963e-01 8.826336e-99
## id feats max.nTuningRuns
## 1 Random.myrandom_classfr .rnorm 0
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 0.291 0.002 0.4859659
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.5164234 0.4555085 0.4893217 0.4
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.6327078 0.4627451 0.4318011
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.4939047 0 0.5064252 0.5480984
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.464752 0.4956046 0.4 0.6314922
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.4614458 0.4271177 0.4960486
## max.Kappa.OOB
## 1 0# ret_lst <- myfit_mdl(mdl_id = "Random", model_method = "myrandom_classfr",
# model_type = glb_model_type,
# indep_vars_vctr = ".rnorm",
# rsp_var = glb_rsp_var, rsp_var_out = glb_rsp_var_out,
# fit_df = glb_fitobs_df, OOB_df = glb_OOBobs_df)
# Any models that have tuning parameters has "better" results with cross-validation
# (except bag & rf) & "different" results for different outcome metrics
# Max.cor.Y
# Check impact of cv
# rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Max.cor.Y.rcv.1X1", type=glb_model_type, trainControl.method="none",
train.method="glmnet")),
indep_vars=max_cor_y_x_vars, rsp_var=glb_rsp_var,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: Max.cor.Y.rcv.1X1.glmnet"
## [1] " indep_vars: biddable,sprice.root2"## Loading required package: glmnet
## Loading required package: Matrix
##
## Attaching package: 'Matrix'
##
## The following object is masked from 'package:tidyr':
##
## expand
##
## Loaded glmnet 2.0-2## Fitting alpha = 0.1, lambda = 0.00576 on full training set
## Length Class Mode
## a0 79 -none- numeric
## beta 158 dgCMatrix S4
## df 79 -none- numeric
## dim 2 -none- numeric
## lambda 79 -none- numeric
## dev.ratio 79 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 2 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) biddable sprice.root2
## 1.0277229 1.8660218 -0.1575942
## [1] "max lambda < lambdaOpt:"
## (Intercept) biddable sprice.root2
## 1.0358390 1.8708816 -0.1583838
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.N
## 1 N 432
## 2 Y 104
## sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.Y
## 1 116
## 2 368
## Prediction
## Reference N Y
## N 432 116
## Y 104 368
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.843137e-01 5.669826e-01 7.577802e-01 8.091950e-01 5.372549e-01
## AccuracyPValue McnemarPValue
## 1.365269e-60 4.583177e-01
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.N
## 1 N 352
## 2 Y 94
## sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.Y
## 1 95
## 2 289
## Prediction
## Reference N Y
## N 352 95
## Y 94 289
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.722892e-01 5.419399e-01 7.422177e-01 8.004125e-01 5.385542e-01
## AccuracyPValue McnemarPValue
## 2.113571e-44 1.000000e+00
## id feats max.nTuningRuns
## 1 Max.cor.Y.rcv.1X1.glmnet biddable,sprice.root2 0
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 0.711 0.022 0.7898522
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.8339416 0.7457627 0.86206 0.4
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.7698745 0.7843137 0.7577802
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.809195 0.5669826 0.7665376 0.8255034
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.7075718 0.8197236 0.4 0.7535854
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.7722892 0.7422177 0.8004125
## max.Kappa.OOB
## 1 0.5419399# rcv_n_folds == 1 & rcv_n_repeats > 1 crashes
for (rcv_n_folds in seq(3, glb_rcv_n_folds + 2, 2))
for (rcv_n_repeats in seq(1, glb_rcv_n_repeats + 2, 2)) {
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = paste0("Max.cor.Y.rcv.", rcv_n_folds, "X", rcv_n_repeats),
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = rcv_n_folds, trainControl.repeats = rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.method = "glmnet", train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize)),
indep_vars = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glb_fitobs_df, OOB_df = glb_OOBobs_df)
}## [1] "fitting model: Max.cor.Y.rcv.3X1.glmnet"
## [1] " indep_vars: biddable,sprice.root2"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.124 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0("Max.cor.Y.rcv.", : model's bestTune found at an
## extreme of tuneGrid for parameter: alpha## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0("Max.cor.Y.rcv.", : model's bestTune found at an
## extreme of tuneGrid for parameter: lambda
## Length Class Mode
## a0 79 -none- numeric
## beta 158 dgCMatrix S4
## df 79 -none- numeric
## dim 2 -none- numeric
## lambda 79 -none- numeric
## dev.ratio 79 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 2 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) biddable sprice.root2
## 0.39977062 1.21150458 -0.08600408
## [1] "max lambda < lambdaOpt:"
## (Intercept) biddable sprice.root2
## 0.4231996 1.2510219 -0.0892442
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.N
## 1 N 457
## 2 Y 118
## sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.Y
## 1 91
## 2 354
## Prediction
## Reference N Y
## N 457 91
## Y 118 354
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.950980e-01 5.862671e-01 7.690020e-01 8.194783e-01 5.372549e-01
## AccuracyPValue McnemarPValue
## 3.331019e-66 7.210452e-02
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.N
## 1 N 342
## 2 Y 89
## sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.Y
## 1 105
## 2 294
## Prediction
## Reference N Y
## N 342 105
## Y 89 294
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.662651e-01 5.311363e-01 7.359543e-01 7.946712e-01 5.385542e-01
## AccuracyPValue McnemarPValue
## 4.058545e-42 2.815083e-01
## id feats max.nTuningRuns
## 1 Max.cor.Y.rcv.3X1.glmnet biddable,sprice.root2 25
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 1.47 0.017 0.7919708
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.8339416 0.75 0.8621335 0.5
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.7720829 0.7950911 0.769002
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.8194783 0.5860164 0.7592654 0.8187919
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.6997389 0.8197353 0.4 0.7519182
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.7662651 0.7359543 0.7946712
## max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1 0.5311363 0.003934893 0.009324777
## [1] "fitting model: Max.cor.Y.rcv.3X3.glmnet"
## [1] " indep_vars: biddable,sprice.root2"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.325, lambda = 0.124 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0("Max.cor.Y.rcv.", : model's bestTune found at an
## extreme of tuneGrid for parameter: lambda
## Length Class Mode
## a0 67 -none- numeric
## beta 134 dgCMatrix S4
## df 67 -none- numeric
## dim 2 -none- numeric
## lambda 67 -none- numeric
## dev.ratio 67 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 2 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) biddable sprice.root2
## 0.36344652 1.18295779 -0.08216503
## [1] "max lambda < lambdaOpt:"
## (Intercept) biddable sprice.root2
## 0.39118688 1.22752496 -0.08592317
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.N
## 1 N 457
## 2 Y 118
## sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.Y
## 1 91
## 2 354
## Prediction
## Reference N Y
## N 457 91
## Y 118 354
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.950980e-01 5.862671e-01 7.690020e-01 8.194783e-01 5.372549e-01
## AccuracyPValue McnemarPValue
## 3.331019e-66 7.210452e-02
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.N
## 1 N 341
## 2 Y 88
## sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.Y
## 1 106
## 2 295
## Prediction
## Reference N Y
## N 341 106
## Y 88 295
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.662651e-01 5.313109e-01 7.359543e-01 7.946712e-01 5.385542e-01
## AccuracyPValue McnemarPValue
## 4.058545e-42 2.222645e-01
## id feats max.nTuningRuns
## 1 Max.cor.Y.rcv.3X3.glmnet biddable,sprice.root2 25
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 2.053 0.016 0.7919708
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.8339416 0.75 0.8622147 0.5
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.7720829 0.7954221 0.769002
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.8194783 0.5866808 0.760384 0.8210291
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.6997389 0.819747 0.4 0.752551
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.7662651 0.7359543 0.7946712
## max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1 0.5313109 0.0200887 0.04087803
## [1] "fitting model: Max.cor.Y.rcv.3X5.glmnet"
## [1] " indep_vars: biddable,sprice.root2"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.55, lambda = 0.124 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0("Max.cor.Y.rcv.", : model's bestTune found at an
## extreme of tuneGrid for parameter: lambda
## Length Class Mode
## a0 61 -none- numeric
## beta 122 dgCMatrix S4
## df 61 -none- numeric
## dim 2 -none- numeric
## lambda 61 -none- numeric
## dev.ratio 61 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 2 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) biddable sprice.root2
## 0.28887900 1.10455877 -0.07355771
## [1] "max lambda < lambdaOpt:"
## (Intercept) biddable sprice.root2
## 0.32191930 1.15672712 -0.07800633
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.N
## 1 N 457
## 2 Y 118
## sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.Y
## 1 91
## 2 354
## Prediction
## Reference N Y
## N 457 91
## Y 118 354
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.950980e-01 5.862671e-01 7.690020e-01 8.194783e-01 5.372549e-01
## AccuracyPValue McnemarPValue
## 3.331019e-66 7.210452e-02
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.N
## 1 N 340
## 2 Y 88
## sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.Y
## 1 107
## 2 295
## Prediction
## Reference N Y
## N 340 107
## Y 88 295
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.650602e-01 5.289828e-01 7.347026e-01 7.935220e-01 5.385542e-01
## AccuracyPValue McnemarPValue
## 1.138374e-41 1.973957e-01
## id feats max.nTuningRuns
## 1 Max.cor.Y.rcv.3X5.glmnet biddable,sprice.root2 25
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 2.454 0.015 0.7919708
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.8339416 0.75 0.8622069 0.5
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.7720829 0.7962655 0.769002
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.8194783 0.58834 0.760384 0.8210291
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.6997389 0.8196593 0.4 0.7515924
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.7650602 0.7347026 0.793522
## max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1 0.5289828 0.01812066 0.03689711
## [1] "fitting model: Max.cor.Y.rcv.5X1.glmnet"
## [1] " indep_vars: biddable,sprice.root2"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.775, lambda = 0.124 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0("Max.cor.Y.rcv.", : model's bestTune found at an
## extreme of tuneGrid for parameter: lambda
## Length Class Mode
## a0 57 -none- numeric
## beta 114 dgCMatrix S4
## df 57 -none- numeric
## dim 2 -none- numeric
## lambda 57 -none- numeric
## dev.ratio 57 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 2 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) biddable sprice.root2
## 0.19570698 1.01735782 -0.06318327
## [1] "max lambda < lambdaOpt:"
## (Intercept) biddable sprice.root2
## 0.23591040 1.07863496 -0.06852179
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.N
## 1 N 461
## 2 Y 118
## sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.Y
## 1 87
## 2 354
## Prediction
## Reference N Y
## N 461 87
## Y 118 354
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.990196e-01 5.939459e-01 7.730893e-01 8.232111e-01 5.372549e-01
## AccuracyPValue McnemarPValue
## 2.536091e-68 3.614514e-02
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.N
## 1 N 339
## 2 Y 88
## sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.Y
## 1 108
## 2 295
## Prediction
## Reference N Y
## N 339 108
## Y 88 295
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.638554e-01 5.266555e-01 7.334512e-01 7.923725e-01 5.385542e-01
## AccuracyPValue McnemarPValue
## 3.171799e-41 1.747358e-01
## id feats max.nTuningRuns
## 1 Max.cor.Y.rcv.5X1.glmnet biddable,sprice.root2 25
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 1.669 0.016 0.7956204
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.8412409 0.75 0.8621064 0.5
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.7754655 0.7990899 0.7730893
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.8232111 0.5944245 0.7615026 0.8232662
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.6997389 0.81956 0.4 0.7506361
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.7638554 0.7334512 0.7923725
## max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1 0.5266555 0.03608644 0.07068726
## [1] "fitting model: Max.cor.Y.rcv.5X3.glmnet"
## [1] " indep_vars: biddable,sprice.root2"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.775, lambda = 0.124 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0("Max.cor.Y.rcv.", : model's bestTune found at an
## extreme of tuneGrid for parameter: lambda
## Length Class Mode
## a0 57 -none- numeric
## beta 114 dgCMatrix S4
## df 57 -none- numeric
## dim 2 -none- numeric
## lambda 57 -none- numeric
## dev.ratio 57 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 2 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) biddable sprice.root2
## 0.19570698 1.01735782 -0.06318327
## [1] "max lambda < lambdaOpt:"
## (Intercept) biddable sprice.root2
## 0.23591040 1.07863496 -0.06852179
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.N
## 1 N 461
## 2 Y 118
## sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.Y
## 1 87
## 2 354
## Prediction
## Reference N Y
## N 461 87
## Y 118 354
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.990196e-01 5.939459e-01 7.730893e-01 8.232111e-01 5.372549e-01
## AccuracyPValue McnemarPValue
## 2.536091e-68 3.614514e-02
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.N
## 1 N 339
## 2 Y 88
## sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.Y
## 1 108
## 2 295
## Prediction
## Reference N Y
## N 339 108
## Y 88 295
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.638554e-01 5.266555e-01 7.334512e-01 7.923725e-01 5.385542e-01
## AccuracyPValue McnemarPValue
## 3.171799e-41 1.747358e-01
## id feats max.nTuningRuns
## 1 Max.cor.Y.rcv.5X3.glmnet biddable,sprice.root2 25
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 2.435 0.015 0.7956204
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.8412409 0.75 0.8621064 0.5
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.7754655 0.7964364 0.7730893
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.8232111 0.5888026 0.7615026 0.8232662
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.6997389 0.81956 0.4 0.7506361
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.7638554 0.7334512 0.7923725
## max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1 0.5266555 0.03349187 0.06729278
## [1] "fitting model: Max.cor.Y.rcv.5X5.glmnet"
## [1] " indep_vars: biddable,sprice.root2"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.124 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0("Max.cor.Y.rcv.", : model's bestTune found at an
## extreme of tuneGrid for parameter: alpha## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0("Max.cor.Y.rcv.", : model's bestTune found at an
## extreme of tuneGrid for parameter: lambda
## Length Class Mode
## a0 54 -none- numeric
## beta 108 dgCMatrix S4
## df 54 -none- numeric
## dim 2 -none- numeric
## lambda 54 -none- numeric
## dev.ratio 54 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 2 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) biddable sprice.root2
## 0.12532879 0.99816406 -0.05704275
## [1] "max lambda < lambdaOpt:"
## (Intercept) biddable sprice.root2
## 0.17511926 1.06475879 -0.06331821
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.N
## 1 N 463
## 2 Y 119
## sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.Y
## 1 85
## 2 353
## Prediction
## Reference N Y
## N 463 85
## Y 119 353
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.000000e-01 5.957477e-01 7.741117e-01 8.241437e-01 5.372549e-01
## AccuracyPValue McnemarPValue
## 7.378773e-69 2.086258e-02
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.N
## 1 N 338
## 2 Y 87
## sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.Y
## 1 109
## 2 296
## Prediction
## Reference N Y
## N 338 109
## Y 87 296
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.638554e-01 5.268317e-01 7.334512e-01 7.923725e-01 5.385542e-01
## AccuracyPValue McnemarPValue
## 3.171799e-41 1.336144e-01
## id feats max.nTuningRuns
## 1 Max.cor.Y.rcv.5X5.glmnet biddable,sprice.root2 25
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 3.132 0.019 0.7963859
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.8448905 0.7478814 0.8621064 0.5
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.7758242 0.7970687 0.7741117
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.8241437 0.5901123 0.7641135 0.8232662
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.7049608 0.819379 0.4 0.751269
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.7638554 0.7334512 0.7923725
## max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1 0.5268317 0.03129087 0.06285747# Add parallel coordinates graph of glb_models_df[, glbMdlMetricsEval] to evaluate cv parameters
tmp_models_cols <- c("id", "max.nTuningRuns",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
print(myplot_parcoord(obs_df = subset(glb_models_df,
grepl("Max.cor.Y.rcv.", id, fixed = TRUE),
select = -feats)[, tmp_models_cols],
id_var = "id"))
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Max.cor.Y.rcv.1X1.cp.0", type=glb_model_type, trainControl.method="none",
train.method="rpart",
tune.df=data.frame(method="rpart", parameter="cp", min=0.0, max=0.0, by=0.1))),
indep_vars=max_cor_y_x_vars, rsp_var=glb_rsp_var,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: Max.cor.Y.rcv.1X1.cp.0.rpart"
## [1] " indep_vars: biddable,sprice.root2"## Loading required package: rpart## Fitting cp = 0 on full training set## Loading required package: rpart.plot
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 1020
##
## CP nsplit rel error
## 1 0.546610169 0 1.0000000
## 2 0.025423729 1 0.4533898
## 3 0.010593220 2 0.4279661
## 4 0.006355932 5 0.3961864
## 5 0.003531073 6 0.3898305
## 6 0.002118644 9 0.3792373
## 7 0.001059322 16 0.3644068
## 8 0.000000000 20 0.3601695
##
## Variable importance
## biddable sprice.root2
## 52 48
##
## Node number 1: 1020 observations, complexity param=0.5466102
## predicted class=N expected loss=0.4627451 P(node) =1
## class counts: 548 472
## probabilities: 0.537 0.463
## left son=2 (554 obs) right son=3 (466 obs)
## Primary splits:
## biddable < 0.5 to the left, improve=169.2715, (0 missing)
## sprice.root2 < 10.23963 to the right, improve=133.6444, (0 missing)
## Surrogate splits:
## sprice.root2 < 12.26518 to the right, agree=0.751, adj=0.455, (0 split)
##
## Node number 2: 554 observations, complexity param=0.003531073
## predicted class=N expected loss=0.198556 P(node) =0.5431373
## class counts: 444 110
## probabilities: 0.801 0.199
## left son=4 (124 obs) right son=5 (430 obs)
## Primary splits:
## sprice.root2 < 19.90914 to the right, improve=3.855349, (0 missing)
##
## Node number 3: 466 observations, complexity param=0.02542373
## predicted class=Y expected loss=0.223176 P(node) =0.4568627
## class counts: 104 362
## probabilities: 0.223 0.777
## left son=6 (164 obs) right son=7 (302 obs)
## Primary splits:
## sprice.root2 < 11.83195 to the right, improve=49.71379, (0 missing)
##
## Node number 4: 124 observations
## predicted class=N expected loss=0.08870968 P(node) =0.1215686
## class counts: 113 11
## probabilities: 0.911 0.089
##
## Node number 5: 430 observations, complexity param=0.003531073
## predicted class=N expected loss=0.2302326 P(node) =0.4215686
## class counts: 331 99
## probabilities: 0.770 0.230
## left son=10 (263 obs) right son=11 (167 obs)
## Primary splits:
## sprice.root2 < 13.61047 to the right, improve=1.786236, (0 missing)
##
## Node number 6: 164 observations, complexity param=0.01059322
## predicted class=N expected loss=0.4634146 P(node) =0.1607843
## class counts: 88 76
## probabilities: 0.537 0.463
## left son=12 (19 obs) right son=13 (145 obs)
## Primary splits:
## sprice.root2 < 20.33187 to the right, improve=2.748634, (0 missing)
##
## Node number 7: 302 observations
## predicted class=Y expected loss=0.05298013 P(node) =0.2960784
## class counts: 16 286
## probabilities: 0.053 0.947
##
## Node number 10: 263 observations
## predicted class=N expected loss=0.1939163 P(node) =0.2578431
## class counts: 212 51
## probabilities: 0.806 0.194
##
## Node number 11: 167 observations, complexity param=0.003531073
## predicted class=N expected loss=0.2874251 P(node) =0.1637255
## class counts: 119 48
## probabilities: 0.713 0.287
## left son=22 (158 obs) right son=23 (9 obs)
## Primary splits:
## sprice.root2 < 13.41548 to the left, improve=4.574556, (0 missing)
##
## Node number 12: 19 observations
## predicted class=N expected loss=0.2105263 P(node) =0.01862745
## class counts: 15 4
## probabilities: 0.789 0.211
##
## Node number 13: 145 observations, complexity param=0.01059322
## predicted class=N expected loss=0.4965517 P(node) =0.1421569
## class counts: 73 72
## probabilities: 0.503 0.497
## left son=26 (87 obs) right son=27 (58 obs)
## Primary splits:
## sprice.root2 < 13.89077 to the right, improve=1.554023, (0 missing)
##
## Node number 22: 158 observations, complexity param=0.002118644
## predicted class=N expected loss=0.2594937 P(node) =0.154902
## class counts: 117 41
## probabilities: 0.741 0.259
## left son=44 (30 obs) right son=45 (128 obs)
## Primary splits:
## sprice.root2 < 12.54832 to the right, improve=1.884019, (0 missing)
##
## Node number 23: 9 observations
## predicted class=Y expected loss=0.2222222 P(node) =0.008823529
## class counts: 2 7
## probabilities: 0.222 0.778
##
## Node number 26: 87 observations, complexity param=0.01059322
## predicted class=N expected loss=0.4367816 P(node) =0.08529412
## class counts: 49 38
## probabilities: 0.563 0.437
## left son=52 (48 obs) right son=53 (39 obs)
## Primary splits:
## sprice.root2 < 16.50717 to the left, improve=2.291777, (0 missing)
##
## Node number 27: 58 observations, complexity param=0.006355932
## predicted class=Y expected loss=0.4137931 P(node) =0.05686275
## class counts: 24 34
## probabilities: 0.414 0.586
## left son=54 (9 obs) right son=55 (49 obs)
## Primary splits:
## sprice.root2 < 12.24684 to the left, improve=1.362421, (0 missing)
##
## Node number 44: 30 observations
## predicted class=N expected loss=0.1 P(node) =0.02941176
## class counts: 27 3
## probabilities: 0.900 0.100
##
## Node number 45: 128 observations, complexity param=0.002118644
## predicted class=N expected loss=0.296875 P(node) =0.1254902
## class counts: 90 38
## probabilities: 0.703 0.297
## left son=90 (95 obs) right son=91 (33 obs)
## Primary splits:
## sprice.root2 < 11.40153 to the left, improve=2.210706, (0 missing)
##
## Node number 52: 48 observations, complexity param=0.001059322
## predicted class=N expected loss=0.3333333 P(node) =0.04705882
## class counts: 32 16
## probabilities: 0.667 0.333
## left son=104 (7 obs) right son=105 (41 obs)
## Primary splits:
## sprice.root2 < 15.89005 to the right, improve=0.5946574, (0 missing)
##
## Node number 53: 39 observations, complexity param=0.002118644
## predicted class=Y expected loss=0.4358974 P(node) =0.03823529
## class counts: 17 22
## probabilities: 0.436 0.564
## left son=106 (15 obs) right son=107 (24 obs)
## Primary splits:
## sprice.root2 < 18.70815 to the right, improve=0.4628205, (0 missing)
##
## Node number 54: 9 observations
## predicted class=N expected loss=0.3333333 P(node) =0.008823529
## class counts: 6 3
## probabilities: 0.667 0.333
##
## Node number 55: 49 observations
## predicted class=Y expected loss=0.3673469 P(node) =0.04803922
## class counts: 18 31
## probabilities: 0.367 0.633
##
## Node number 90: 95 observations, complexity param=0.002118644
## predicted class=N expected loss=0.2421053 P(node) =0.09313725
## class counts: 72 23
## probabilities: 0.758 0.242
## left son=180 (23 obs) right son=181 (72 obs)
## Primary splits:
## sprice.root2 < 10.23963 to the right, improve=1.460984, (0 missing)
##
## Node number 91: 33 observations, complexity param=0.002118644
## predicted class=N expected loss=0.4545455 P(node) =0.03235294
## class counts: 18 15
## probabilities: 0.545 0.455
## left son=182 (26 obs) right son=183 (7 obs)
## Primary splits:
## sprice.root2 < 11.81057 to the right, improve=1.198801, (0 missing)
##
## Node number 104: 7 observations
## predicted class=N expected loss=0.1428571 P(node) =0.006862745
## class counts: 6 1
## probabilities: 0.857 0.143
##
## Node number 105: 41 observations, complexity param=0.001059322
## predicted class=N expected loss=0.3658537 P(node) =0.04019608
## class counts: 26 15
## probabilities: 0.634 0.366
## left son=210 (32 obs) right son=211 (9 obs)
## Primary splits:
## sprice.root2 < 15.8106 to the left, improve=0.8299458, (0 missing)
##
## Node number 106: 15 observations
## predicted class=N expected loss=0.4666667 P(node) =0.01470588
## class counts: 8 7
## probabilities: 0.533 0.467
##
## Node number 107: 24 observations
## predicted class=Y expected loss=0.375 P(node) =0.02352941
## class counts: 9 15
## probabilities: 0.375 0.625
##
## Node number 180: 23 observations
## predicted class=N expected loss=0.08695652 P(node) =0.02254902
## class counts: 21 2
## probabilities: 0.913 0.087
##
## Node number 181: 72 observations, complexity param=0.002118644
## predicted class=N expected loss=0.2916667 P(node) =0.07058824
## class counts: 51 21
## probabilities: 0.708 0.292
## left son=362 (10 obs) right son=363 (62 obs)
## Primary splits:
## sprice.root2 < 5.417281 to the left, improve=0.8532258, (0 missing)
##
## Node number 182: 26 observations
## predicted class=N expected loss=0.3846154 P(node) =0.0254902
## class counts: 16 10
## probabilities: 0.615 0.385
##
## Node number 183: 7 observations
## predicted class=Y expected loss=0.2857143 P(node) =0.006862745
## class counts: 2 5
## probabilities: 0.286 0.714
##
## Node number 210: 32 observations
## predicted class=N expected loss=0.3125 P(node) =0.03137255
## class counts: 22 10
## probabilities: 0.688 0.312
##
## Node number 211: 9 observations
## predicted class=Y expected loss=0.4444444 P(node) =0.008823529
## class counts: 4 5
## probabilities: 0.444 0.556
##
## Node number 362: 10 observations
## predicted class=N expected loss=0.1 P(node) =0.009803922
## class counts: 9 1
## probabilities: 0.900 0.100
##
## Node number 363: 62 observations, complexity param=0.002118644
## predicted class=N expected loss=0.3225806 P(node) =0.06078431
## class counts: 42 20
## probabilities: 0.677 0.323
## left son=726 (55 obs) right son=727 (7 obs)
## Primary splits:
## sprice.root2 < 7.602942 to the right, improve=2.42145, (0 missing)
##
## Node number 726: 55 observations, complexity param=0.001059322
## predicted class=N expected loss=0.2727273 P(node) =0.05392157
## class counts: 40 15
## probabilities: 0.727 0.273
## left son=1452 (29 obs) right son=1453 (26 obs)
## Primary splits:
## sprice.root2 < 9.486569 to the left, improve=1.234627, (0 missing)
##
## Node number 727: 7 observations
## predicted class=Y expected loss=0.2857143 P(node) =0.006862745
## class counts: 2 5
## probabilities: 0.286 0.714
##
## Node number 1452: 29 observations
## predicted class=N expected loss=0.1724138 P(node) =0.02843137
## class counts: 24 5
## probabilities: 0.828 0.172
##
## Node number 1453: 26 observations, complexity param=0.001059322
## predicted class=N expected loss=0.3846154 P(node) =0.0254902
## class counts: 16 10
## probabilities: 0.615 0.385
## left son=2906 (19 obs) right son=2907 (7 obs)
## Primary splits:
## sprice.root2 < 9.565528 to the right, improve=0.6685946, (0 missing)
##
## Node number 2906: 19 observations
## predicted class=N expected loss=0.3157895 P(node) =0.01862745
## class counts: 13 6
## probabilities: 0.684 0.316
##
## Node number 2907: 7 observations
## predicted class=Y expected loss=0.4285714 P(node) =0.006862745
## class counts: 3 4
## probabilities: 0.429 0.571
##
## n= 1020
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 1020 472 N (0.53725490 0.46274510)
## 2) biddable< 0.5 554 110 N (0.80144404 0.19855596)
## 4) sprice.root2>=19.90914 124 11 N (0.91129032 0.08870968) *
## 5) sprice.root2< 19.90914 430 99 N (0.76976744 0.23023256)
## 10) sprice.root2>=13.61047 263 51 N (0.80608365 0.19391635) *
## 11) sprice.root2< 13.61047 167 48 N (0.71257485 0.28742515)
## 22) sprice.root2< 13.41548 158 41 N (0.74050633 0.25949367)
## 44) sprice.root2>=12.54832 30 3 N (0.90000000 0.10000000) *
## 45) sprice.root2< 12.54832 128 38 N (0.70312500 0.29687500)
## 90) sprice.root2< 11.40153 95 23 N (0.75789474 0.24210526)
## 180) sprice.root2>=10.23963 23 2 N (0.91304348 0.08695652) *
## 181) sprice.root2< 10.23963 72 21 N (0.70833333 0.29166667)
## 362) sprice.root2< 5.417281 10 1 N (0.90000000 0.10000000) *
## 363) sprice.root2>=5.417281 62 20 N (0.67741935 0.32258065)
## 726) sprice.root2>=7.602942 55 15 N (0.72727273 0.27272727)
## 1452) sprice.root2< 9.486569 29 5 N (0.82758621 0.17241379) *
## 1453) sprice.root2>=9.486569 26 10 N (0.61538462 0.38461538)
## 2906) sprice.root2>=9.565528 19 6 N (0.68421053 0.31578947) *
## 2907) sprice.root2< 9.565528 7 3 Y (0.42857143 0.57142857) *
## 727) sprice.root2< 7.602942 7 2 Y (0.28571429 0.71428571) *
## 91) sprice.root2>=11.40153 33 15 N (0.54545455 0.45454545)
## 182) sprice.root2>=11.81057 26 10 N (0.61538462 0.38461538) *
## 183) sprice.root2< 11.81057 7 2 Y (0.28571429 0.71428571) *
## 23) sprice.root2>=13.41548 9 2 Y (0.22222222 0.77777778) *
## 3) biddable>=0.5 466 104 Y (0.22317597 0.77682403)
## 6) sprice.root2>=11.83195 164 76 N (0.53658537 0.46341463)
## 12) sprice.root2>=20.33187 19 4 N (0.78947368 0.21052632) *
## 13) sprice.root2< 20.33187 145 72 N (0.50344828 0.49655172)
## 26) sprice.root2>=13.89077 87 38 N (0.56321839 0.43678161)
## 52) sprice.root2< 16.50717 48 16 N (0.66666667 0.33333333)
## 104) sprice.root2>=15.89005 7 1 N (0.85714286 0.14285714) *
## 105) sprice.root2< 15.89005 41 15 N (0.63414634 0.36585366)
## 210) sprice.root2< 15.8106 32 10 N (0.68750000 0.31250000) *
## 211) sprice.root2>=15.8106 9 4 Y (0.44444444 0.55555556) *
## 53) sprice.root2>=16.50717 39 17 Y (0.43589744 0.56410256)
## 106) sprice.root2>=18.70815 15 7 N (0.53333333 0.46666667) *
## 107) sprice.root2< 18.70815 24 9 Y (0.37500000 0.62500000) *
## 27) sprice.root2< 13.89077 58 24 Y (0.41379310 0.58620690)
## 54) sprice.root2< 12.24684 9 3 N (0.66666667 0.33333333) *
## 55) sprice.root2>=12.24684 49 18 Y (0.36734694 0.63265306) *
## 7) sprice.root2< 11.83195 302 16 Y (0.05298013 0.94701987) *
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.N
## 1 N 484
## 2 Y 107
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.Y
## 1 64
## 2 365
## Prediction
## Reference N Y
## N 484 64
## Y 107 365
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.323529e-01 6.606905e-01 8.079872e-01 8.547824e-01 5.372549e-01
## AccuracyPValue McnemarPValue
## 4.198532e-88 1.318969e-03
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.N
## 1 N 327
## 2 Y 93
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.Y
## 1 120
## 2 290
## Prediction
## Reference N Y
## N 327 120
## Y 93 290
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.433735e-01 4.862697e-01 7.122254e-01 7.727821e-01 5.385542e-01
## AccuracyPValue McnemarPValue
## 4.300069e-34 7.483233e-02
## id feats max.nTuningRuns
## 1 Max.cor.Y.rcv.1X1.cp.0.rpart biddable,sprice.root2 0
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 0.772 0.015 0.8281424
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.8978102 0.7584746 0.8816304 0.4
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.8102109 0.8323529 0.8079872
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.8547824 0.6606905 0.750495 0.8456376
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.6553525 0.7934621 0.3 0.7313997
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.7433735 0.7122254 0.7727821
## max.Kappa.OOB
## 1 0.4862697#stop(here"); glb_to_sav(); all.equal(glb_models_df, sav_models_df)
# if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Max.cor.Y",
type=glb_model_type, trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method="rpart")),
indep_vars=max_cor_y_x_vars, rsp_var=glb_rsp_var,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: Max.cor.Y.rpart"
## [1] " indep_vars: biddable,sprice.root2"
## + Fold1.Rep1: cp=0.003531
## - Fold1.Rep1: cp=0.003531
## + Fold2.Rep1: cp=0.003531
## - Fold2.Rep1: cp=0.003531
## + Fold3.Rep1: cp=0.003531
## - Fold3.Rep1: cp=0.003531
## + Fold1.Rep2: cp=0.003531
## - Fold1.Rep2: cp=0.003531
## + Fold2.Rep2: cp=0.003531
## - Fold2.Rep2: cp=0.003531
## + Fold3.Rep2: cp=0.003531
## - Fold3.Rep2: cp=0.003531
## + Fold1.Rep3: cp=0.003531
## - Fold1.Rep3: cp=0.003531
## + Fold2.Rep3: cp=0.003531
## - Fold2.Rep3: cp=0.003531
## + Fold3.Rep3: cp=0.003531
## - Fold3.Rep3: cp=0.003531
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.00636 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 1020
##
## CP nsplit rel error
## 1 0.546610169 0 1.0000000
## 2 0.025423729 1 0.4533898
## 3 0.010593220 2 0.4279661
## 4 0.006355932 5 0.3961864
##
## Variable importance
## biddable sprice.root2
## 56 44
##
## Node number 1: 1020 observations, complexity param=0.5466102
## predicted class=N expected loss=0.4627451 P(node) =1
## class counts: 548 472
## probabilities: 0.537 0.463
## left son=2 (554 obs) right son=3 (466 obs)
## Primary splits:
## biddable < 0.5 to the left, improve=169.2715, (0 missing)
## sprice.root2 < 10.23963 to the right, improve=133.6444, (0 missing)
## Surrogate splits:
## sprice.root2 < 12.26518 to the right, agree=0.751, adj=0.455, (0 split)
##
## Node number 2: 554 observations
## predicted class=N expected loss=0.198556 P(node) =0.5431373
## class counts: 444 110
## probabilities: 0.801 0.199
##
## Node number 3: 466 observations, complexity param=0.02542373
## predicted class=Y expected loss=0.223176 P(node) =0.4568627
## class counts: 104 362
## probabilities: 0.223 0.777
## left son=6 (164 obs) right son=7 (302 obs)
## Primary splits:
## sprice.root2 < 11.83195 to the right, improve=49.71379, (0 missing)
##
## Node number 6: 164 observations, complexity param=0.01059322
## predicted class=N expected loss=0.4634146 P(node) =0.1607843
## class counts: 88 76
## probabilities: 0.537 0.463
## left son=12 (19 obs) right son=13 (145 obs)
## Primary splits:
## sprice.root2 < 20.33187 to the right, improve=2.748634, (0 missing)
##
## Node number 7: 302 observations
## predicted class=Y expected loss=0.05298013 P(node) =0.2960784
## class counts: 16 286
## probabilities: 0.053 0.947
##
## Node number 12: 19 observations
## predicted class=N expected loss=0.2105263 P(node) =0.01862745
## class counts: 15 4
## probabilities: 0.789 0.211
##
## Node number 13: 145 observations, complexity param=0.01059322
## predicted class=N expected loss=0.4965517 P(node) =0.1421569
## class counts: 73 72
## probabilities: 0.503 0.497
## left son=26 (87 obs) right son=27 (58 obs)
## Primary splits:
## sprice.root2 < 13.89077 to the right, improve=1.554023, (0 missing)
##
## Node number 26: 87 observations, complexity param=0.01059322
## predicted class=N expected loss=0.4367816 P(node) =0.08529412
## class counts: 49 38
## probabilities: 0.563 0.437
## left son=52 (48 obs) right son=53 (39 obs)
## Primary splits:
## sprice.root2 < 16.50717 to the left, improve=2.291777, (0 missing)
##
## Node number 27: 58 observations
## predicted class=Y expected loss=0.4137931 P(node) =0.05686275
## class counts: 24 34
## probabilities: 0.414 0.586
##
## Node number 52: 48 observations
## predicted class=N expected loss=0.3333333 P(node) =0.04705882
## class counts: 32 16
## probabilities: 0.667 0.333
##
## Node number 53: 39 observations
## predicted class=Y expected loss=0.4358974 P(node) =0.03823529
## class counts: 17 22
## probabilities: 0.436 0.564
##
## n= 1020
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 1020 472 N (0.53725490 0.46274510)
## 2) biddable< 0.5 554 110 N (0.80144404 0.19855596) *
## 3) biddable>=0.5 466 104 Y (0.22317597 0.77682403)
## 6) sprice.root2>=11.83195 164 76 N (0.53658537 0.46341463)
## 12) sprice.root2>=20.33187 19 4 N (0.78947368 0.21052632) *
## 13) sprice.root2< 20.33187 145 72 N (0.50344828 0.49655172)
## 26) sprice.root2>=13.89077 87 38 N (0.56321839 0.43678161)
## 52) sprice.root2< 16.50717 48 16 N (0.66666667 0.33333333) *
## 53) sprice.root2>=16.50717 39 17 Y (0.43589744 0.56410256) *
## 27) sprice.root2< 13.89077 58 24 Y (0.41379310 0.58620690) *
## 7) sprice.root2< 11.83195 302 16 Y (0.05298013 0.94701987) *
## sold.fctr sold.fctr.predict.Max.cor.Y.rpart.N
## 1 N 491
## 2 Y 130
## sold.fctr.predict.Max.cor.Y.rpart.Y
## 1 57
## 2 342
## Prediction
## Reference N Y
## N 491 57
## Y 130 342
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.166667e-01 6.272891e-01 7.915283e-01 8.399619e-01 5.372549e-01
## AccuracyPValue McnemarPValue
## 2.183067e-78 1.400662e-07
## sold.fctr sold.fctr.predict.Max.cor.Y.rpart.N
## 1 N 368
## 2 Y 112
## sold.fctr.predict.Max.cor.Y.rpart.Y
## 1 79
## 2 271
## Prediction
## Reference N Y
## N 368 79
## Y 112 271
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.698795e-01 5.341327e-01 7.397113e-01 7.981170e-01 5.385542e-01
## AccuracyPValue McnemarPValue
## 1.766677e-43 2.058893e-02
## id feats max.nTuningRuns
## 1 Max.cor.Y.rpart biddable,sprice.root2 5
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 1.655 0.016 0.8102808
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.8959854 0.7245763 0.8387008 0.5
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.7853042 0.7964073 0.7915283
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.8399619 0.5852116 0.7445254 0.8545861
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.6344648 0.7855854 0.3 0.739427
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.7698795 0.7397113 0.798117
## max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1 0.5341327 0.02006899 0.04005915if (!is.null(glb_date_vars) &&
(sum(grepl(paste(glb_date_vars, "\\.day\\.minutes\\.poly\\.", sep=""),
names(glb_allobs_df))) > 0)) {
# ret_lst <- myfit_mdl(mdl_id="Max.cor.Y.TmSrs.poly1",
# model_method=ifelse(glb_is_regression, "lm",
# ifelse(glb_is_binomial, "glm", "rpart")),
# model_type=glb_model_type,
# indep_vars_vctr=c(max_cor_y_x_vars, paste0(glb_date_vars, ".day.minutes")),
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_rcv_n_folds, tune_models_df=NULL)
#
ret_lst <- myfit_mdl(mdl_id="Max.cor.Y.TmSrs.poly",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr=c(max_cor_y_x_vars,
grep(paste(glb_date_vars, "\\.day\\.minutes\\.poly\\.", sep=""),
names(glb_allobs_df), value=TRUE)),
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_rcv_n_folds, tune_models_df=NULL)
}
# Interactions.High.cor.Y
if (length(int_feats <- setdiff(setdiff(unique(glb_feats_df$cor.high.X), NA),
subset(glb_feats_df, nzv)$id)) > 0) {
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Interact.High.cor.Y",
type=glb_model_type, trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method="glmnet")),
indep_vars=c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":")),
rsp_var=glb_rsp_var,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
} ## [1] "fitting model: Interact.High.cor.Y.glmnet"
## [1] " indep_vars: biddable,sprice.root2,biddable:sprice.log10,biddable:D.terms.post.stop.n.log,biddable:startprice.dcm2.is9,biddable:D.weight.post.stem.sum,biddable:D.chrs.n.log,biddable:cellular.fctr,biddable:D.terms.post.stem.n.log,biddable:sprice.root2"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.325, lambda = 0.00124 on full training set
## Length Class Mode
## a0 100 -none- numeric
## beta 1100 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 11 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) biddable
## 0.01092205 4.74489316
## sprice.root2 biddable:D.chrs.n.log
## -0.08925084 0.07353070
## biddable:D.terms.post.stop.n.log biddable:D.weight.post.stem.sum
## -0.03122283 -0.14511418
## biddable:cellular.fctr1 biddable:cellular.fctrUnknown
## -0.13728156 -1.05190220
## biddable:sprice.log10 biddable:sprice.root2
## -0.10196500 -0.15254153
## biddable:startprice.dcm2.is9
## -0.04646263
## [1] "max lambda < lambdaOpt:"
## (Intercept) biddable
## -0.002342312 4.814280953
## sprice.root2 biddable:D.chrs.n.log
## -0.088443437 0.099967006
## biddable:D.terms.post.stop.n.log biddable:D.weight.post.stem.sum
## -0.057642263 -0.156438317
## biddable:cellular.fctr1 biddable:cellular.fctrUnknown
## -0.141662526 -1.060718650
## biddable:sprice.log10 biddable:sprice.root2
## -0.113943290 -0.152574470
## biddable:startprice.dcm2.is9
## -0.056816165
## sold.fctr sold.fctr.predict.Interact.High.cor.Y.glmnet.N
## 1 N 467
## 2 Y 124
## sold.fctr.predict.Interact.High.cor.Y.glmnet.Y
## 1 81
## 2 348
## Prediction
## Reference N Y
## N 467 81
## Y 124 348
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.990196e-01 5.932255e-01 7.730893e-01 8.232111e-01 5.372549e-01
## AccuracyPValue McnemarPValue
## 2.536091e-68 3.352638e-03
## sold.fctr sold.fctr.predict.Interact.High.cor.Y.glmnet.N
## 1 N 349
## 2 Y 93
## sold.fctr.predict.Interact.High.cor.Y.glmnet.Y
## 1 98
## 2 290
## Prediction
## Reference N Y
## N 349 98
## Y 93 290
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.698795e-01 5.374385e-01 7.397113e-01 7.981170e-01 5.385542e-01
## AccuracyPValue McnemarPValue
## 1.766677e-43 7.722525e-01
## id
## 1 Interact.High.cor.Y.glmnet
## feats
## 1 biddable,sprice.root2,biddable:sprice.log10,biddable:D.terms.post.stop.n.log,biddable:startprice.dcm2.is9,biddable:D.weight.post.stem.sum,biddable:D.chrs.n.log,biddable:cellular.fctr,biddable:D.terms.post.stem.n.log,biddable:sprice.root2
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 2.693 0.096
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.7970973 0.8886861 0.7055085 0.863937
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.772475 0.8026098
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.7730893 0.8232111 0.5988972
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.7551533 0.8680089 0.6422977 0.8189
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.3 0.7522698 0.7698795
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7397113 0.798117 0.5374385
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01796977 0.03705653# Low.cor.X
# if (glb_is_classification && glb_is_binomial)
# indep_vars_vctr <- subset(glb_feats_df, is.na(cor.high.X) &
# is.ConditionalX.y &
# (exclude.as.feat != 1))[, "id"] else
indep_vars <- subset(glb_feats_df, is.na(cor.high.X) & !nzv &
(exclude.as.feat != 1))[, "id"]
indep_vars <- myadjust_interaction_feats(indep_vars)
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Low.cor.X",
type=glb_model_type, trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method="glmnet")),
indep_vars=indep_vars, rsp_var=glb_rsp_var,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: Low.cor.X.glmnet"
## [1] " indep_vars: biddable,spdiff.cut.fctr,startprice.dcm2.is9,D.wrds.stop.n.log,D.weight.sum.stem.stop.Ratio,D.ratio.weight.sum.wrds.n,.rnorm,storage.fctr,D.chrs.pnct11.n.log,startprice.dgt2.is9,color.fctr,prdl.my.fctr,D.terms.post.stop.n.log,cellular.fctr,startprice.dgt1.is9,condition.fctr,sprice.root2,prdl.my.fctr:.clusterid.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.775, lambda = 0.00124 on full training set
## Length Class Mode
## a0 86 -none- numeric
## beta 5332 dgCMatrix S4
## df 86 -none- numeric
## dim 2 -none- numeric
## lambda 86 -none- numeric
## dev.ratio 86 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 62 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## 0.28483910
## .rnorm
## -0.02167279
## D.chrs.pnct11.n.log
## 0.40659853
## D.ratio.weight.sum.wrds.n
## -0.11673547
## D.terms.post.stop.n.log
## -0.04986918
## D.weight.sum.stem.stop.Ratio
## -1.64844144
## D.wrds.stop.n.log
## -0.06590812
## biddable
## 2.44322986
## cellular.fctrUnknown
## -1.42750817
## color.fctrGold
## 0.05191392
## color.fctrSpace Gray
## -0.31389140
## color.fctrUnknown
## 0.32139639
## color.fctrWhite
## -0.14084948
## condition.fctrFor parts or not working
## -0.63820054
## condition.fctrManufacturer refurbished
## -1.03703740
## condition.fctrNew other (see details)
## 0.53706086
## condition.fctrSeller refurbished
## -0.92779447
## prdl.my.fctriPad1
## -0.73094246
## prdl.my.fctriPad2
## -0.50827810
## prdl.my.fctriPad3
## 0.09236029
## prdl.my.fctriPad4
## 1.02122755
## prdl.my.fctriPadAir
## 1.27964442
## prdl.my.fctriPadAir2
## 1.73963473
## prdl.my.fctriPadmini
## -0.45817873
## prdl.my.fctriPadmini2
## 1.07559291
## prdl.my.fctriPadmini3
## 0.58081997
## spdiff.cut.fctr(-100,-10]
## 2.03718267
## spdiff.cut.fctr(-10,-1]
## 3.78016051
## spdiff.cut.fctr(-1,0]
## 4.91900841
## spdiff.cut.fctr(0,1]
## 4.02364901
## spdiff.cut.fctr(1,10]
## 4.05473201
## spdiff.cut.fctr(10,100]
## 3.51590467
## spdiff.cut.fctr(100,1e+03]
## 0.83208831
## sprice.root2
## -0.17520239
## startprice.dcm2.is9
## -0.37526422
## startprice.dgt1.is9
## -0.12236933
## startprice.dgt2.is9
## 0.12473613
## storage.fctr16
## -0.10386074
## storage.fctr64
## 0.19776093
## storage.fctrUnknown
## 1.37356185
## prdl.my.fctrUnknown:.clusterid.fctr2
## 0.75846077
## prdl.my.fctriPad1:.clusterid.fctr2
## -0.47363807
## prdl.my.fctriPad3:.clusterid.fctr2
## 0.23980635
## prdl.my.fctriPad4:.clusterid.fctr2
## -1.15493595
## prdl.my.fctriPadAir:.clusterid.fctr2
## -0.30219482
## prdl.my.fctriPadmini2:.clusterid.fctr2
## -0.59647290
## prdl.my.fctrUnknown:.clusterid.fctr3
## -0.71399278
## prdl.my.fctriPad1:.clusterid.fctr3
## -0.74280799
## prdl.my.fctriPad4:.clusterid.fctr3
## -3.99203955
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## 0.30739060
## .rnorm
## -0.02307603
## D.chrs.pnct11.n.log
## 0.41194798
## D.ratio.weight.sum.wrds.n
## -0.11841828
## D.terms.post.stop.n.log
## -0.04857498
## D.weight.sum.stem.stop.Ratio
## -1.70659855
## D.wrds.stop.n.log
## -0.07092480
## biddable
## 2.45597112
## cellular.fctrUnknown
## -1.44850328
## color.fctrGold
## 0.06040873
## color.fctrSpace Gray
## -0.31733631
## color.fctrUnknown
## 0.32580290
## color.fctrWhite
## -0.14400741
## condition.fctrFor parts or not working
## -0.64810440
## condition.fctrManufacturer refurbished
## -1.05310177
## condition.fctrNew other (see details)
## 0.54221686
## condition.fctrSeller refurbished
## -0.94083418
## prdl.my.fctriPad1
## -0.72962954
## prdl.my.fctriPad2
## -0.50283877
## prdl.my.fctriPad3
## 0.10518937
## prdl.my.fctriPad4
## 1.05514873
## prdl.my.fctriPadAir
## 1.31094762
## prdl.my.fctriPadAir2
## 1.76759552
## prdl.my.fctriPadmini
## -0.45080046
## prdl.my.fctriPadmini2
## 1.10294883
## prdl.my.fctriPadmini3
## 0.60534029
## spdiff.cut.fctr(-100,-10]
## 2.07034040
## spdiff.cut.fctr(-10,-1]
## 3.81948497
## spdiff.cut.fctr(-1,0]
## 4.97713395
## spdiff.cut.fctr(0,1]
## 4.07618809
## spdiff.cut.fctr(1,10]
## 4.09721806
## spdiff.cut.fctr(10,100]
## 3.55524679
## spdiff.cut.fctr(100,1e+03]
## 0.86203115
## sprice.root2
## -0.17628994
## startprice.dcm2.is9
## -0.38089328
## startprice.dgt1.is9
## -0.12199526
## startprice.dgt2.is9
## 0.12986601
## storage.fctr16
## -0.10644192
## storage.fctr64
## 0.19978492
## storage.fctrUnknown
## 1.40216390
## prdl.my.fctrUnknown:.clusterid.fctr2
## 0.77627886
## prdl.my.fctriPad1:.clusterid.fctr2
## -0.48242024
## prdl.my.fctriPad3:.clusterid.fctr2
## 0.24204519
## prdl.my.fctriPad4:.clusterid.fctr2
## -1.18680995
## prdl.my.fctriPadAir:.clusterid.fctr2
## -0.31435860
## prdl.my.fctriPadmini2:.clusterid.fctr2
## -0.60900002
## prdl.my.fctrUnknown:.clusterid.fctr3
## -0.72474486
## prdl.my.fctriPad1:.clusterid.fctr3
## -0.75028690
## prdl.my.fctriPad4:.clusterid.fctr3
## -4.10529031
## sold.fctr sold.fctr.predict.Low.cor.X.glmnet.N
## 1 N 493
## 2 Y 85
## sold.fctr.predict.Low.cor.X.glmnet.Y
## 1 55
## 2 387
## Prediction
## Reference N Y
## N 493 55
## Y 85 387
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.627451e-01 7.227357e-01 8.400893e-01 8.832827e-01 5.372549e-01
## AccuracyPValue McnemarPValue
## 3.342931e-109 1.424808e-02
## sold.fctr sold.fctr.predict.Low.cor.X.glmnet.N
## 1 N 313
## 2 Y 58
## sold.fctr.predict.Low.cor.X.glmnet.Y
## 1 134
## 2 325
## Prediction
## Reference N Y
## N 313 134
## Y 58 325
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.686747e-01 5.411011e-01 7.384586e-01 7.969687e-01 5.385542e-01
## AccuracyPValue McnemarPValue
## 5.056117e-43 6.209575e-08
## id
## 1 Low.cor.X.glmnet
## feats
## 1 biddable,spdiff.cut.fctr,startprice.dcm2.is9,D.wrds.stop.n.log,D.weight.sum.stem.stop.Ratio,D.ratio.weight.sum.wrds.n,.rnorm,storage.fctr,D.chrs.pnct11.n.log,startprice.dgt2.is9,color.fctr,prdl.my.fctr,D.terms.post.stop.n.log,cellular.fctr,startprice.dgt1.is9,condition.fctr,sprice.root2,prdl.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 3.008 0.116
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8597751 0.899635 0.8199153 0.9329264
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.8468271 0.8349657
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8400893 0.8832827 0.6666179
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.7801532 0.8187919 0.7415144 0.8727052
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.3 0.7719715 0.7686747
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7384586 0.7969687 0.5411011
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.009378705 0.01938402rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor label_minor bgn end elapsed
## 10 fit.models 6 0 0 311.876 377.259 65.383
## 11 fit.models 6 1 1 377.259 NA NAfit.models_1_chunk_df <- myadd_chunk(NULL, "fit.models_1_bgn", label.minor="setup")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 setup 387.899 NA NA#stop(here"); glb_to_sav(); all.equal(glb_models_df, sav_models_df)
topindep_var <- NULL; interact_vars <- NULL;
for (mdl_id_pfx in names(glb_mdl_family_lst)) {
fit.models_1_chunk_df <-
myadd_chunk(fit.models_1_chunk_df, paste0("fit.models_1_", mdl_id_pfx),
major.inc = FALSE, label.minor = "setup")
indep_vars <- NULL;
if (grepl("\\.Interact", mdl_id_pfx)) {
if (is.null(topindep_var) && is.null(interact_vars)) {
# select best glmnet model upto now
dsp_models_df <- orderBy(model_sel_frmla <- get_model_sel_frmla(),
glb_models_df)
dsp_models_df <- subset(dsp_models_df,
grepl(".glmnet", id, fixed = TRUE))
bst_mdl_id <- dsp_models_df$id[1]
mdl_id_pfx <-
paste(c(head(unlist(strsplit(bst_mdl_id, "[.]")), -1), "Interact"),
collapse=".")
# select most importance feature
if (is.null(bst_featsimp_df <-
myget_feats_importance(glb_models_lst[[bst_mdl_id]]))) {
warning("Base model for RFE.Interact: ", bst_mdl_id,
" has no important features")
next
}
topindep_ix <- 1
while (is.null(topindep_var) && (topindep_ix <= nrow(bst_featsimp_df))) {
topindep_var <- row.names(bst_featsimp_df)[topindep_ix]
if (grepl(".fctr", topindep_var, fixed=TRUE))
topindep_var <-
paste0(unlist(strsplit(topindep_var, ".fctr"))[1], ".fctr")
if (topindep_var %in% names(glb_interaction_only_feats_lst)) {
topindep_var <- NULL; topindep_ix <- topindep_ix + 1
} else break
}
# select features with importance > max(10, importance of .rnorm) & is not highest
# combine factor dummy features to just the factor feature
if (length(pos_rnorm <-
grep(".rnorm", row.names(bst_featsimp_df), fixed=TRUE)) > 0)
imp_rnorm <- bst_featsimp_df[pos_rnorm, 1] else
imp_rnorm <- NA
importance_cutoff <- max(10, imp_rnorm, na.rm=TRUE)
interact_vars <-
tail(row.names(subset(bst_featsimp_df,
importance > importance_cutoff)), -1)
if (length(interact_vars) > 0) {
interact_vars <-
myadjust_interaction_feats(myextract_actual_feats(interact_vars))
interact_vars <-
interact_vars[!grepl(topindep_var, interact_vars, fixed=TRUE)]
}
### bid0_sp only
# interact_vars <- c(
# "biddable", "D.ratio.sum.TfIdf.wrds.n", "D.TfIdf.sum.stem.stop.Ratio", "D.sum.TfIdf",
# "D.TfIdf.sum.post.stop", "D.TfIdf.sum.post.stem", "D.ratio.wrds.stop.n.wrds.n", "D.chrs.uppr.n.log",
# "D.chrs.n.log", "color.fctr"
# # , "condition.fctr", "prdl.my.descr.fctr"
# )
# interact_vars <- setdiff(interact_vars, c("startprice.dgt2.is9", "color.fctr"))
###
indep_vars <- myextract_actual_feats(row.names(bst_featsimp_df))
indep_vars <- setdiff(indep_vars, topindep_var)
if (length(interact_vars) > 0) {
indep_vars <-
setdiff(indep_vars, myextract_actual_feats(interact_vars))
indep_vars <- c(indep_vars,
paste(topindep_var, setdiff(interact_vars, topindep_var),
sep = "*"))
} else indep_vars <- union(indep_vars, topindep_var)
}
}
if (is.null(indep_vars))
indep_vars <- glb_mdl_feats_lst[[mdl_id_pfx]]
if (is.null(indep_vars) && grepl("RFE\\.", mdl_id_pfx))
indep_vars <- myextract_actual_feats(predictors(rfe_fit_results))
if (is.null(indep_vars))
indep_vars <- subset(glb_feats_df, !nzv & (exclude.as.feat != 1))[, "id"]
if (grepl("^%<d-%", indep_vars)) {
#stop(here")
indep_vars <-
eval(parse(text = str_trim(unlist(strsplit(indep_vars, "%<d-%"))[2])))
}
indep_vars <- myadjust_interaction_feats(indep_vars)
if (grepl("\\.Interact", mdl_id_pfx)) {
# if (method != tail(unlist(strsplit(bst_mdl_id, "[.]")), 1)) next
if (is.null(glb_mdl_family_lst[[mdl_id_pfx]])) {
if (!is.null(glb_mdl_family_lst[["Best.Interact"]]))
glb_mdl_family_lst[[mdl_id_pfx]] <-
glb_mdl_family_lst[["Best.Interact"]]
}
}
if (!is.null(glbObsFitOutliers[[mdl_id_pfx]])) {
fitobs_df <- glb_fitobs_df[!(glb_fitobs_df[, glb_id_var] %in%
glbObsFitOutliers[[mdl_id_pfx]]), ]
} else fitobs_df <- glb_fitobs_df
if (is.null(glb_mdl_family_lst[[mdl_id_pfx]]))
mdl_methods <- glbMdlMethods else
mdl_methods <- glb_mdl_family_lst[[mdl_id_pfx]]
for (method in mdl_methods) {
if (method %in% c("rpart", "rf")) {
# rpart: fubar's the tree
# rf: skip the scenario w/ .rnorm for speed
indep_vars <- setdiff(indep_vars, c(".rnorm"))
#mdl_id <- paste0(mdl_id_pfx, ".no.rnorm")
}
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df,
paste0("fit.models_1_", mdl_id_pfx), major.inc = FALSE,
label.minor = method)
ret_lst <-
myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = mdl_id_pfx,
type = glb_model_type, tune.df = glb_tune_models_df,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
#trainControl.allowParallel = FALSE,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = method)),
indep_vars = indep_vars, rsp_var = glb_rsp_var,
fit_df = fitobs_df, OOB_df = glb_OOBobs_df)
}
}## label step_major step_minor label_minor bgn end
## 1 fit.models_1_bgn 1 0 setup 387.899 387.909
## 2 fit.models_1_CSM.X 1 1 setup 387.910 NA
## elapsed
## 1 0.01
## 2 NA
## label step_major step_minor label_minor bgn end
## 2 fit.models_1_CSM.X 1 1 setup 387.910 387.922
## 3 fit.models_1_CSM.X 1 2 bagEarth 387.923 NA
## elapsed
## 2 0.012
## 3 NA
## [1] "fitting model: CSM.X.bagEarth"
## [1] " indep_vars: spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr"## Loading required package: earth
## Loading required package: plotmo
## Loading required package: plotrix
##
## Attaching package: 'plotrix'
##
## The following object is masked from 'package:gplots':
##
## plotCI
##
## Loading required package: TeachingDemos## + : degree=1, nprune=64## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## - : degree=1, nprune=64
## Aggregating results
## Fitting final model on full training set## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred##
## Call:
## bagEarth.default(x = structure(c(-0.480112420809766, 1.43504225194153, 2.52526248536899, -1.6375712221881, -0.89892418133905, 0.367913716767642, 1.54830639736751, -0.368003285764661, -0.482448574433828, -0.458778254303339, 0.895917830346375, 1.27864282766749, -0.521798717011069, 1.09047221934056, 1.87462218220423, -0.816487211016767, 0.0810553378009752, 1.03341902803144, -1.66333330096743, -0.503451670272392, -0.914308401049965, -1.75677234237956, -0.384349130016673, 0.031671391224914, 1.16314925979891, -0.77802211523859, -1.26735911622058, 0.0738291506769477, -1.0538922620204, 0.546280137629045, 0.999685015258156, 0.636598885177834, 0.379500784049766, -0.680825135178795, 1.36007418916595, -1.03227718276357, -0.883918863763299, -0.693984880518334, 1.08812088345302, -0.743052878778297, -0.244494683584288, -0.725294091780842, 0.785335863072958, -0.553209374946575, -0.559644955122013, 1.1842966803235, 0.422970906063789, 0.336755545604089, 1.42493068703692, -0.864669104597755, 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"1710", "1711", "1713", "1717", "1718", "1723", "1724", "1725", "1726", "1730", "1731", "1732", "1733", "1734", "1736", "1737", "1740", "1744", "1745", "1746", "1747", "1748", "1749", "1751", "1753", "1754", "1755", "1759", "1760", "1761", "1762", "1768", "1769", "1774", "1776", "1777", "1778", "1779", "1780", "1782", "1791", "1792", "1793", "1794", "1797", "1799", "1803", "1804", "1805", "1808", "1809", "1813", "1816", "1817", "1822", "1823", "1824", "1827", "1828", "1829", "1830", "1834", "1835", "1836", "1838", "1839", "1840", "1842", "1843", "1845", "1846", "1847", "1848", "1850")), degree = 1, nprune = 64, keepxy = TRUE, glm = structure(list( family = function (link = "logit") { linktemp <- substitute(link) if (!is.character(linktemp)) linktemp <- deparse(linktemp) okLinks <- c("logit", "probit", "cloglog", "cauchit", "log") if (linktemp %in% okLinks) stats <- make.link(linktemp) else if (is.character(link)) { stats <- make.link(link) linktemp <- link } else { if (inherits(link, "link-glm")) { stats <- link if (!is.null(stats$name)) linktemp <- stats$name } else { stop(gettextf("link \"%s\" not available for binomial family; available links are %s", linktemp, paste(sQuote(okLinks), collapse = ", ")), domain = NA) } } variance <- function(mu) mu * (1 - mu) validmu <- function(mu) all(is.finite(mu)) && all(mu > 0 & mu < 1) dev.resids <- function(y, mu, wt) .Call(C_binomial_dev_resids, y, mu, wt) aic <- function(y, n, mu, wt, dev) { m <- if (any(n > 1)) n else wt -2 * sum(ifelse(m > 0, (wt/m), 0) * dbinom(round(m * y), round(m), mu, log = TRUE)) } initialize <- expression({ if (NCOL(y) == 1) { if (is.factor(y)) y <- y != levels(y)[1L] n <- rep.int(1, nobs) y[weights == 0] <- 0 if (any(y < 0 | y > 1)) stop("y values must be 0 <= y <= 1") mustart <- (weights * y + 0.5)/(weights + 1) m <- weights * y if (any(abs(m - round(m)) > 0.001)) warning("non-integer #successes in a binomial glm!") } else if (NCOL(y) == 2) { if (any(abs(y - round(y)) > 0.001)) warning("non-integer counts in a binomial glm!") n <- y[, 1] + y[, 2] y <- ifelse(n == 0, 0, y[, 1]/n) weights <- weights * n mustart <- (n * y + 0.5)/(n + 1) } else stop("for the 'binomial' family, y must be a vector of 0 and 1's\nor a 2 column matrix where col 1 is no. successes and col 2 is no. failures") }) simfun <- function(object, nsim) { ftd <- fitted(object) n <- length(ftd) ntot <- n * nsim wts <- object$prior.weights if (any(wts%%1 != 0)) stop("cannot simulate from non-integer prior.weights") if (!is.null(m <- object$model)) { y <- model.response(m) if (is.factor(y)) { yy <- factor(1 + rbinom(ntot, size = 1, prob = ftd), labels = levels(y)) split(yy, rep(seq_len(nsim), each = n)) } else if (is.matrix(y) && ncol(y) == 2) { yy <- vector("list", nsim) for (i in seq_len(nsim)) { Y <- rbinom(n, size = wts, prob = ftd) YY <- cbind(Y, wts - Y) colnames(YY) <- colnames(y) yy[[i]] <- YY } yy } else rbinom(ntot, size = wts, prob = ftd)/wts } else rbinom(ntot, size = wts, prob = ftd)/wts } structure(list(family = "binomial", link = linktemp, linkfun = stats$linkfun, linkinv = stats$linkinv, variance = variance, dev.resids = dev.resids, aic = aic, mu.eta = stats$mu.eta, initialize = initialize, validmu = validmu, valideta = stats$valideta, simulate = simfun), class = "family") }), .Names = "family"))
##
## Out of bag statistics:
##
## Accuracy Kappa
## 0% 0.7578 0.5146
## 2.5% 0.7635 0.5256
## 25% 0.7895 0.5772
## 50% 0.7997 0.5954
## 75% 0.8128 0.6253
## 97.5% 0.8383 0.6763
## 100% 0.8496 0.6945
##
## Model Selection Statistics:
##
## Num Terms Num Variables
## Length:1548 Length:1548
## Class :character Class :character
## Mode :character Mode :character
## sold.fctr sold.fctr.predict.CSM.X.bagEarth.N
## 1 N 507
## 2 Y 67
## sold.fctr.predict.CSM.X.bagEarth.Y
## 1 41
## 2 403
## Prediction
## Reference N Y
## N 507 41
## Y 67 403
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.939096e-01 7.857226e-01 8.733491e-01 9.121576e-01 5.383104e-01
## AccuracyPValue McnemarPValue
## 1.629850e-133 1.614478e-02
## sold.fctr sold.fctr.predict.CSM.X.bagEarth.N
## 1 N 380
## 2 Y 93
## sold.fctr.predict.CSM.X.bagEarth.Y
## 1 67
## 2 290
## Prediction
## Reference N Y
## N 380 67
## Y 93 290
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.072289e-01 6.102577e-01 7.787174e-01 8.335386e-01 5.385542e-01
## AccuracyPValue McnemarPValue
## 3.756564e-59 4.810683e-02
## id
## 1 CSM.X.bagEarth
## feats
## 1 spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 119.149 58.531
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8913146 0.9251825 0.8574468 0.958499
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.8818381 0.8024729
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8733491 0.9121576 0.601862
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.803646 0.8501119 0.7571802 0.8766246
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.7837838 0.8072289
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7787174 0.8335386 0.6102577
## label step_major step_minor label_minor bgn end
## 3 fit.models_1_CSM.X 1 2 bagEarth 387.923 529.527
## 4 fit.models_1_CSM.X 1 3 glm 529.527 NA
## elapsed
## 3 141.604
## 4 NA
## [1] "fitting model: CSM.X.glm"
## [1] " indep_vars: spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr"
## + Fold1.Rep1: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold1.Rep1: parameter=none
## + Fold2.Rep1: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold2.Rep1: parameter=none
## + Fold3.Rep1: parameter=none## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold3.Rep1: parameter=none
## + Fold1.Rep2: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold1.Rep2: parameter=none
## + Fold2.Rep2: parameter=none## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold2.Rep2: parameter=none
## + Fold3.Rep2: parameter=none## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold3.Rep2: parameter=none
## + Fold1.Rep3: parameter=none## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold1.Rep3: parameter=none
## + Fold2.Rep3: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold2.Rep3: parameter=none
## + Fold3.Rep3: parameter=none## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold3.Rep3: parameter=none
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.9951 -0.4610 -0.0689 0.3072 3.5806
##
## Coefficients: (28 not defined because of singularities)
## Estimate Std. Error z value
## (Intercept) -2.534e+01 3.036e+01 -0.835
## .rnorm -7.376e-04 1.069e-01 -0.007
## D.chrs.n.log -6.853e+00 4.527e+00 -1.514
## D.chrs.pnct11.n.log 7.259e-01 4.189e-01 1.733
## D.chrs.pnct13.n.log 1.522e-01 4.156e-01 0.366
## D.chrs.uppr.n.log 4.708e+00 4.188e+00 1.124
## D.ratio.weight.sum.wrds.n 3.187e-01 3.177e-01 1.003
## D.ratio.wrds.stop.n.wrds.n -1.009e+01 4.057e+00 -2.486
## D.terms.post.stem.n.log 2.404e+01 1.136e+01 2.116
## D.terms.post.stop.n.log -3.068e+01 1.133e+01 -2.707
## D.weight.post.stem.sum -8.044e+00 5.488e+00 -1.466
## D.weight.post.stop.sum 7.495e+00 5.296e+00 1.415
## D.weight.sum NA NA NA
## D.weight.sum.stem.stop.Ratio 3.708e+01 3.067e+01 1.209
## D.wrds.n.log 7.777e+00 2.586e+00 3.007
## D.wrds.stop.n.log 3.127e-01 7.062e-01 0.443
## D.wrds.unq.n.log NA NA NA
## cellular.fctr1 -2.012e-01 3.624e-01 -0.555
## cellular.fctrUnknown -1.939e+00 6.540e-01 -2.965
## color.fctrGold 2.206e-01 7.308e-01 0.302
## `color.fctrSpace Gray` -3.252e-01 4.348e-01 -0.748
## color.fctrUnknown 3.218e-01 2.989e-01 1.077
## color.fctrWhite -7.425e-02 3.317e-01 -0.224
## `condition.fctrFor parts or not working` -1.081e+00 4.664e-01 -2.318
## `condition.fctrManufacturer refurbished` -1.430e+00 8.480e-01 -1.686
## condition.fctrNew 1.071e-01 4.185e-01 0.256
## `condition.fctrNew other (see details)` 6.522e-01 5.885e-01 1.108
## `condition.fctrSeller refurbished` -1.198e+00 4.871e-01 -2.459
## prdl.my.fctriPad1 -1.049e+00 7.238e-01 -1.449
## prdl.my.fctriPad2 -6.181e-01 6.314e-01 -0.979
## prdl.my.fctriPad3 3.197e-02 6.661e-01 0.048
## prdl.my.fctriPad4 1.326e+00 7.339e-01 1.807
## prdl.my.fctriPadAir 1.407e+00 7.298e-01 1.928
## prdl.my.fctriPadAir2 1.620e+00 8.115e-01 1.997
## prdl.my.fctriPadmini -8.001e-01 6.171e-01 -1.296
## prdl.my.fctriPadmini2 9.873e-01 7.530e-01 1.311
## prdl.my.fctriPadmini3 4.599e-01 8.289e-01 0.555
## `spdiff.cut.fctr(-100,-10]` 2.904e+00 1.080e+00 2.688
## `spdiff.cut.fctr(-10,-1]` 4.960e+00 1.152e+00 4.306
## `spdiff.cut.fctr(-1,0]` 5.233e+00 1.445e+00 3.622
## `spdiff.cut.fctr(0,1]` 3.539e+00 1.500e+00 2.360
## `spdiff.cut.fctr(1,10]` 5.006e+00 1.134e+00 4.413
## `spdiff.cut.fctr(10,100]` 4.148e+00 1.108e+00 3.745
## `spdiff.cut.fctr(100,1e+03]` 4.907e-01 1.455e+00 0.337
## biddable 2.989e+00 1.122e+00 2.663
## sprice.d20nexp -2.334e+00 3.998e+00 -0.584
## sprice.log10 -9.107e-01 1.360e+00 -0.670
## sprice.root2 -8.983e-02 1.970e-01 -0.456
## startprice.dcm1.is9 -4.864e-01 4.160e-01 -1.169
## startprice.dcm2.is9 -6.291e-02 4.047e-01 -0.155
## startprice.dgt1.is9 3.646e-02 2.983e-01 0.122
## startprice.dgt2.is9 1.840e-02 3.319e-01 0.055
## storage.fctr16 -2.351e-01 7.740e-01 -0.304
## storage.fctr32 -3.624e-02 7.813e-01 -0.046
## storage.fctr64 1.995e-01 7.631e-01 0.261
## storage.fctrUnknown 1.764e+00 9.936e-01 1.775
## `cellular.fctr0:carrier.fctrNone` NA NA NA
## `cellular.fctr1:carrier.fctrNone` NA NA NA
## `cellular.fctrUnknown:carrier.fctrNone` NA NA NA
## `cellular.fctr0:carrier.fctrOther` NA NA NA
## `cellular.fctr1:carrier.fctrOther` NA NA NA
## `cellular.fctrUnknown:carrier.fctrOther` NA NA NA
## `cellular.fctr0:carrier.fctrSprint` NA NA NA
## `cellular.fctr1:carrier.fctrSprint` 1.038e+00 7.864e-01 1.320
## `cellular.fctrUnknown:carrier.fctrSprint` NA NA NA
## `cellular.fctr0:carrier.fctrT-Mobile` NA NA NA
## `cellular.fctr1:carrier.fctrT-Mobile` -4.095e-02 1.306e+00 -0.031
## `cellular.fctrUnknown:carrier.fctrT-Mobile` NA NA NA
## `cellular.fctr0:carrier.fctrUnknown` NA NA NA
## `cellular.fctr1:carrier.fctrUnknown` -1.561e-01 6.324e-01 -0.247
## `cellular.fctrUnknown:carrier.fctrUnknown` NA NA NA
## `cellular.fctr0:carrier.fctrVerizon` NA NA NA
## `cellular.fctr1:carrier.fctrVerizon` 4.866e-01 5.143e-01 0.946
## `cellular.fctrUnknown:carrier.fctrVerizon` NA NA NA
## `prdl.my.fctrUnknown:.clusterid.fctr2` 7.660e-01 8.958e-01 0.855
## `prdl.my.fctriPad1:.clusterid.fctr2` -1.163e+00 8.743e-01 -1.330
## `prdl.my.fctriPad2:.clusterid.fctr2` NA NA NA
## `prdl.my.fctriPad3:.clusterid.fctr2` 1.287e-01 9.694e-01 0.133
## `prdl.my.fctriPad4:.clusterid.fctr2` -2.008e+00 1.349e+00 -1.489
## `prdl.my.fctriPadAir:.clusterid.fctr2` -2.493e-01 9.445e-01 -0.264
## `prdl.my.fctriPadAir2:.clusterid.fctr2` NA NA NA
## `prdl.my.fctriPadmini:.clusterid.fctr2` NA NA NA
## `prdl.my.fctriPadmini2:.clusterid.fctr2` -6.533e-01 1.497e+00 -0.436
## `prdl.my.fctriPadmini3:.clusterid.fctr2` NA NA NA
## `prdl.my.fctrUnknown:.clusterid.fctr3` 2.297e-01 1.322e+00 0.174
## `prdl.my.fctriPad1:.clusterid.fctr3` -1.433e+00 9.152e-01 -1.566
## `prdl.my.fctriPad2:.clusterid.fctr3` NA NA NA
## `prdl.my.fctriPad3:.clusterid.fctr3` NA NA NA
## `prdl.my.fctriPad4:.clusterid.fctr3` -1.866e+01 1.328e+03 -0.014
## `prdl.my.fctriPadAir:.clusterid.fctr3` NA NA NA
## `prdl.my.fctriPadAir2:.clusterid.fctr3` NA NA NA
## `prdl.my.fctriPadmini:.clusterid.fctr3` NA NA NA
## `prdl.my.fctriPadmini2:.clusterid.fctr3` NA NA NA
## `prdl.my.fctriPadmini3:.clusterid.fctr3` NA NA NA
## `spdiff.cut.fctr(-100,-10]:biddable` -7.490e-01 1.156e+00 -0.648
## `spdiff.cut.fctr(-10,-1]:biddable` -1.593e+00 1.288e+00 -1.237
## `spdiff.cut.fctr(-1,0]:biddable` 1.313e+01 7.667e+02 0.017
## `spdiff.cut.fctr(0,1]:biddable` NA NA NA
## `spdiff.cut.fctr(1,10]:biddable` -4.988e-01 1.384e+00 -0.360
## `spdiff.cut.fctr(10,100]:biddable` 6.409e-01 1.369e+00 0.468
## `spdiff.cut.fctr(100,1e+03]:biddable` 2.024e+00 1.887e+00 1.073
## Pr(>|z|)
## (Intercept) 0.403975
## .rnorm 0.994495
## D.chrs.n.log 0.130055
## D.chrs.pnct11.n.log 0.083098 .
## D.chrs.pnct13.n.log 0.714250
## D.chrs.uppr.n.log 0.260846
## D.ratio.weight.sum.wrds.n 0.315694
## D.ratio.wrds.stop.n.wrds.n 0.012924 *
## D.terms.post.stem.n.log 0.034346 *
## D.terms.post.stop.n.log 0.006791 **
## D.weight.post.stem.sum 0.142671
## D.weight.post.stop.sum 0.157002
## D.weight.sum NA
## D.weight.sum.stem.stop.Ratio 0.226733
## D.wrds.n.log 0.002637 **
## D.wrds.stop.n.log 0.657965
## D.wrds.unq.n.log NA
## cellular.fctr1 0.578864
## cellular.fctrUnknown 0.003029 **
## color.fctrGold 0.762739
## `color.fctrSpace Gray` 0.454569
## color.fctrUnknown 0.281600
## color.fctrWhite 0.822863
## `condition.fctrFor parts or not working` 0.020463 *
## `condition.fctrManufacturer refurbished` 0.091711 .
## condition.fctrNew 0.798084
## `condition.fctrNew other (see details)` 0.267743
## `condition.fctrSeller refurbished` 0.013931 *
## prdl.my.fctriPad1 0.147243
## prdl.my.fctriPad2 0.327600
## prdl.my.fctriPad3 0.961723
## prdl.my.fctriPad4 0.070814 .
## prdl.my.fctriPadAir 0.053895 .
## prdl.my.fctriPadAir2 0.045875 *
## prdl.my.fctriPadmini 0.194831
## prdl.my.fctriPadmini2 0.189801
## prdl.my.fctriPadmini3 0.579019
## `spdiff.cut.fctr(-100,-10]` 0.007186 **
## `spdiff.cut.fctr(-10,-1]` 1.66e-05 ***
## `spdiff.cut.fctr(-1,0]` 0.000292 ***
## `spdiff.cut.fctr(0,1]` 0.018267 *
## `spdiff.cut.fctr(1,10]` 1.02e-05 ***
## `spdiff.cut.fctr(10,100]` 0.000180 ***
## `spdiff.cut.fctr(100,1e+03]` 0.735883
## biddable 0.007739 **
## sprice.d20nexp 0.559333
## sprice.log10 0.503165
## sprice.root2 0.648412
## startprice.dcm1.is9 0.242249
## startprice.dcm2.is9 0.876458
## startprice.dgt1.is9 0.902723
## startprice.dgt2.is9 0.955785
## storage.fctr16 0.761374
## storage.fctr32 0.963005
## storage.fctr64 0.793745
## storage.fctrUnknown 0.075907 .
## `cellular.fctr0:carrier.fctrNone` NA
## `cellular.fctr1:carrier.fctrNone` NA
## `cellular.fctrUnknown:carrier.fctrNone` NA
## `cellular.fctr0:carrier.fctrOther` NA
## `cellular.fctr1:carrier.fctrOther` NA
## `cellular.fctrUnknown:carrier.fctrOther` NA
## `cellular.fctr0:carrier.fctrSprint` NA
## `cellular.fctr1:carrier.fctrSprint` 0.186775
## `cellular.fctrUnknown:carrier.fctrSprint` NA
## `cellular.fctr0:carrier.fctrT-Mobile` NA
## `cellular.fctr1:carrier.fctrT-Mobile` 0.974986
## `cellular.fctrUnknown:carrier.fctrT-Mobile` NA
## `cellular.fctr0:carrier.fctrUnknown` NA
## `cellular.fctr1:carrier.fctrUnknown` 0.805038
## `cellular.fctrUnknown:carrier.fctrUnknown` NA
## `cellular.fctr0:carrier.fctrVerizon` NA
## `cellular.fctr1:carrier.fctrVerizon` 0.344063
## `cellular.fctrUnknown:carrier.fctrVerizon` NA
## `prdl.my.fctrUnknown:.clusterid.fctr2` 0.392474
## `prdl.my.fctriPad1:.clusterid.fctr2` 0.183371
## `prdl.my.fctriPad2:.clusterid.fctr2` NA
## `prdl.my.fctriPad3:.clusterid.fctr2` 0.894403
## `prdl.my.fctriPad4:.clusterid.fctr2` 0.136522
## `prdl.my.fctriPadAir:.clusterid.fctr2` 0.791807
## `prdl.my.fctriPadAir2:.clusterid.fctr2` NA
## `prdl.my.fctriPadmini:.clusterid.fctr2` NA
## `prdl.my.fctriPadmini2:.clusterid.fctr2` 0.662523
## `prdl.my.fctriPadmini3:.clusterid.fctr2` NA
## `prdl.my.fctrUnknown:.clusterid.fctr3` 0.862077
## `prdl.my.fctriPad1:.clusterid.fctr3` 0.117291
## `prdl.my.fctriPad2:.clusterid.fctr3` NA
## `prdl.my.fctriPad3:.clusterid.fctr3` NA
## `prdl.my.fctriPad4:.clusterid.fctr3` 0.988790
## `prdl.my.fctriPadAir:.clusterid.fctr3` NA
## `prdl.my.fctriPadAir2:.clusterid.fctr3` NA
## `prdl.my.fctriPadmini:.clusterid.fctr3` NA
## `prdl.my.fctriPadmini2:.clusterid.fctr3` NA
## `prdl.my.fctriPadmini3:.clusterid.fctr3` NA
## `spdiff.cut.fctr(-100,-10]:biddable` 0.517090
## `spdiff.cut.fctr(-10,-1]:biddable` 0.216143
## `spdiff.cut.fctr(-1,0]:biddable` 0.986335
## `spdiff.cut.fctr(0,1]:biddable` NA
## `spdiff.cut.fctr(1,10]:biddable` 0.718621
## `spdiff.cut.fctr(10,100]:biddable` 0.639629
## `spdiff.cut.fctr(100,1e+03]:biddable` 0.283336
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1405.3 on 1017 degrees of freedom
## Residual deviance: 622.0 on 945 degrees of freedom
## AIC: 768
##
## Number of Fisher Scoring iterations: 17## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## sold.fctr sold.fctr.predict.CSM.X.glm.N sold.fctr.predict.CSM.X.glm.Y
## 1 N 473 75
## 2 Y 63 407
## Prediction
## Reference N Y
## N 473 75
## Y 63 407
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.644401e-01 7.277737e-01 8.418654e-01 8.848822e-01 5.383104e-01
## AccuracyPValue McnemarPValue
## 1.196932e-109 3.490764e-01## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## sold.fctr sold.fctr.predict.CSM.X.glm.N sold.fctr.predict.CSM.X.glm.Y
## 1 N 368 79
## 2 Y 98 285
## Prediction
## Reference N Y
## N 368 79
## Y 98 285
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.867470e-01 5.694138e-01 7.572842e-01 8.141569e-01 5.385542e-01
## AccuracyPValue McnemarPValue
## 3.470669e-50 1.760675e-01
## id
## 1 CSM.X.glm
## feats
## 1 spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 3.088 0.233
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8654721 0.9032847 0.8276596 0.9415592
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.855042 0.8264355
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8418654 0.8848822 0.6495141
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.7836958 0.8232662 0.7441253 0.8705557
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.7630522 0.786747
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7572842 0.8141569 0.5694138
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01401451 0.02849804
## label step_major step_minor label_minor bgn end
## 4 fit.models_1_CSM.X 1 3 glm 529.527 537.022
## 5 fit.models_1_CSM.X 1 4 bayesglm 537.023 NA
## elapsed
## 4 7.496
## 5 NA
## [1] "fitting model: CSM.X.bayesglm"
## [1] " indep_vars: spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr"## Loading required package: arm
## Loading required package: MASS
##
## Attaching package: 'MASS'
##
## The following object is masked from 'package:dplyr':
##
## select
##
## Loading required package: lme4
##
## arm (Version 1.8-6, built: 2015-7-7)
##
## Working directory is /Users/bbalaji-2012/Documents/Work/Courses/MIT/Analytics_Edge_15_071x/Assignments/Kaggle_eBay_iPads
##
##
## Attaching package: 'arm'
##
## The following object is masked from 'package:plotrix':
##
## rescale
## + Fold1.Rep1: parameter=none
## - Fold1.Rep1: parameter=none
## + Fold2.Rep1: parameter=none
## - Fold2.Rep1: parameter=none
## + Fold3.Rep1: parameter=none
## - Fold3.Rep1: parameter=none
## + Fold1.Rep2: parameter=none
## - Fold1.Rep2: parameter=none
## + Fold2.Rep2: parameter=none
## - Fold2.Rep2: parameter=none
## + Fold3.Rep2: parameter=none
## - Fold3.Rep2: parameter=none
## + Fold1.Rep3: parameter=none
## - Fold1.Rep3: parameter=none
## + Fold2.Rep3: parameter=none
## - Fold2.Rep3: parameter=none
## + Fold3.Rep3: parameter=none
## - Fold3.Rep3: parameter=none
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.9644 -0.5162 -0.1415 0.3313 3.1116
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) 4.992425 5.392851 0.926
## .rnorm -0.014947 0.100521 -0.149
## D.chrs.n.log -0.109506 0.443933 -0.247
## D.chrs.pnct11.n.log 0.471843 0.357723 1.319
## D.chrs.pnct13.n.log -0.128948 0.364241 -0.354
## D.chrs.uppr.n.log -0.002766 0.456843 -0.006
## D.ratio.weight.sum.wrds.n -0.044275 0.262600 -0.169
## D.ratio.wrds.stop.n.wrds.n -1.696522 1.990751 -0.852
## D.terms.post.stem.n.log -0.168282 0.838724 -0.201
## D.terms.post.stop.n.log -0.514153 0.879693 -0.584
## D.weight.post.stem.sum -0.084764 0.385173 -0.220
## D.weight.post.stop.sum -0.029820 0.366278 -0.081
## D.weight.sum -0.084764 0.385173 -0.220
## D.weight.sum.stem.stop.Ratio -2.469281 4.474959 -0.552
## D.wrds.n.log 0.719823 0.830014 0.867
## D.wrds.stop.n.log 0.098319 0.441955 0.222
## D.wrds.unq.n.log -0.168282 0.838724 -0.201
## cellular.fctr1 0.215160 1.305972 0.165
## cellular.fctrUnknown -0.696642 1.697430 -0.410
## color.fctrGold 0.153540 0.643808 0.238
## `color.fctrSpace Gray` -0.340914 0.396746 -0.859
## color.fctrUnknown 0.335023 0.274929 1.219
## color.fctrWhite -0.147517 0.304784 -0.484
## `condition.fctrFor parts or not working` -0.710070 0.411105 -1.727
## `condition.fctrManufacturer refurbished` -1.015964 0.752348 -1.350
## condition.fctrNew 0.025220 0.382932 0.066
## `condition.fctrNew other (see details)` 0.622987 0.520324 1.197
## `condition.fctrSeller refurbished` -0.960331 0.441851 -2.173
## prdl.my.fctriPad1 -1.037679 0.563038 -1.843
## prdl.my.fctriPad2 -0.658665 0.486615 -1.354
## prdl.my.fctriPad3 -0.057819 0.518200 -0.112
## prdl.my.fctriPad4 0.885129 0.575678 1.538
## prdl.my.fctriPadAir 1.029870 0.564723 1.824
## prdl.my.fctriPadAir2 1.440673 0.613541 2.348
## prdl.my.fctriPadmini -0.689411 0.474225 -1.454
## prdl.my.fctriPadmini2 0.888959 0.592422 1.501
## prdl.my.fctriPadmini3 0.425890 0.660073 0.645
## `spdiff.cut.fctr(-100,-10]` 1.870669 0.540346 3.462
## `spdiff.cut.fctr(-10,-1]` 3.705475 0.618228 5.994
## `spdiff.cut.fctr(-1,0]` 4.208958 0.975860 4.313
## `spdiff.cut.fctr(0,1]` 1.545049 1.823379 0.847
## `spdiff.cut.fctr(1,10]` 3.675157 0.608006 6.045
## `spdiff.cut.fctr(10,100]` 3.003757 0.565183 5.315
## `spdiff.cut.fctr(100,1e+03]` -0.213175 0.876823 -0.243
## biddable 2.008265 0.540727 3.714
## sprice.d20nexp -0.750446 1.775518 -0.423
## sprice.log10 -0.474807 0.472319 -1.005
## sprice.root2 -0.129197 0.080515 -1.605
## startprice.dcm1.is9 -0.418773 0.374939 -1.117
## startprice.dcm2.is9 -0.102623 0.370040 -0.277
## startprice.dgt1.is9 0.059102 0.274008 0.216
## startprice.dgt2.is9 0.061299 0.306635 0.200
## storage.fctr16 -0.317515 0.570788 -0.556
## storage.fctr32 -0.170821 0.584616 -0.292
## storage.fctr64 0.093364 0.573550 0.163
## storage.fctrUnknown 1.402447 0.771005 1.819
## `cellular.fctr0:carrier.fctrNone` 0.368068 1.301730 0.283
## `cellular.fctr1:carrier.fctrNone` 0.000000 2.500000 0.000
## `cellular.fctrUnknown:carrier.fctrNone` 0.000000 2.500000 0.000
## `cellular.fctr0:carrier.fctrOther` 0.000000 2.500000 0.000
## `cellular.fctr1:carrier.fctrOther` 0.000000 2.500000 0.000
## `cellular.fctrUnknown:carrier.fctrOther` 0.000000 2.500000 0.000
## `cellular.fctr0:carrier.fctrSprint` 0.000000 2.500000 0.000
## `cellular.fctr1:carrier.fctrSprint` 0.964904 0.713342 1.353
## `cellular.fctrUnknown:carrier.fctrSprint` 0.000000 2.500000 0.000
## `cellular.fctr0:carrier.fctrT-Mobile` 0.000000 2.500000 0.000
## `cellular.fctr1:carrier.fctrT-Mobile` -0.271792 1.025430 -0.265
## `cellular.fctrUnknown:carrier.fctrT-Mobile` 0.000000 2.500000 0.000
## `cellular.fctr0:carrier.fctrUnknown` 0.000000 2.500000 0.000
## `cellular.fctr1:carrier.fctrUnknown` -0.198461 0.547046 -0.363
## `cellular.fctrUnknown:carrier.fctrUnknown` -0.696642 1.697430 -0.410
## `cellular.fctr0:carrier.fctrVerizon` 0.000000 2.500000 0.000
## `cellular.fctr1:carrier.fctrVerizon` 0.313670 0.461955 0.679
## `cellular.fctrUnknown:carrier.fctrVerizon` 0.000000 2.500000 0.000
## `prdl.my.fctrUnknown:.clusterid.fctr2` 0.794118 0.783655 1.013
## `prdl.my.fctriPad1:.clusterid.fctr2` -0.427318 0.741722 -0.576
## `prdl.my.fctriPad2:.clusterid.fctr2` 0.000000 2.500000 0.000
## `prdl.my.fctriPad3:.clusterid.fctr2` 0.295946 0.836562 0.354
## `prdl.my.fctriPad4:.clusterid.fctr2` -0.892319 1.072120 -0.832
## `prdl.my.fctriPadAir:.clusterid.fctr2` -0.128302 0.807275 -0.159
## `prdl.my.fctriPadAir2:.clusterid.fctr2` 0.000000 2.500000 0.000
## `prdl.my.fctriPadmini:.clusterid.fctr2` 0.000000 2.500000 0.000
## `prdl.my.fctriPadmini2:.clusterid.fctr2` -0.311835 1.076712 -0.290
## `prdl.my.fctriPadmini3:.clusterid.fctr2` 0.000000 2.500000 0.000
## `prdl.my.fctrUnknown:.clusterid.fctr3` -0.466405 1.038422 -0.449
## `prdl.my.fctriPad1:.clusterid.fctr3` -0.926064 0.759466 -1.219
## `prdl.my.fctriPad2:.clusterid.fctr3` 0.000000 2.500000 0.000
## `prdl.my.fctriPad3:.clusterid.fctr3` 0.000000 2.500000 0.000
## `prdl.my.fctriPad4:.clusterid.fctr3` -3.193528 1.626670 -1.963
## `prdl.my.fctriPadAir:.clusterid.fctr3` 0.000000 2.500000 0.000
## `prdl.my.fctriPadAir2:.clusterid.fctr3` 0.000000 2.500000 0.000
## `prdl.my.fctriPadmini:.clusterid.fctr3` 0.000000 2.500000 0.000
## `prdl.my.fctriPadmini2:.clusterid.fctr3` 0.000000 2.500000 0.000
## `prdl.my.fctriPadmini3:.clusterid.fctr3` 0.000000 2.500000 0.000
## `spdiff.cut.fctr(-100,-10]:biddable` 0.152722 0.612018 0.250
## `spdiff.cut.fctr(-10,-1]:biddable` -0.441449 0.761004 -0.580
## `spdiff.cut.fctr(-1,0]:biddable` 0.972950 1.744738 0.558
## `spdiff.cut.fctr(0,1]:biddable` 1.545049 1.823379 0.847
## `spdiff.cut.fctr(1,10]:biddable` 0.499781 0.865815 0.577
## `spdiff.cut.fctr(10,100]:biddable` 1.522358 0.841264 1.810
## `spdiff.cut.fctr(100,1e+03]:biddable` 2.258390 1.234891 1.829
## Pr(>|z|)
## (Intercept) 0.354577
## .rnorm 0.881795
## D.chrs.n.log 0.805163
## D.chrs.pnct11.n.log 0.187163
## D.chrs.pnct13.n.log 0.723325
## D.chrs.uppr.n.log 0.995170
## D.ratio.weight.sum.wrds.n 0.866109
## D.ratio.wrds.stop.n.wrds.n 0.394102
## D.terms.post.stem.n.log 0.840980
## D.terms.post.stop.n.log 0.558905
## D.weight.post.stem.sum 0.825818
## D.weight.post.stop.sum 0.935113
## D.weight.sum 0.825818
## D.weight.sum.stem.stop.Ratio 0.581086
## D.wrds.n.log 0.385809
## D.wrds.stop.n.log 0.823952
## D.wrds.unq.n.log 0.840980
## cellular.fctr1 0.869140
## cellular.fctrUnknown 0.681505
## color.fctrGold 0.811503
## `color.fctrSpace Gray` 0.390188
## color.fctrUnknown 0.223005
## color.fctrWhite 0.628382
## `condition.fctrFor parts or not working` 0.084128 .
## `condition.fctrManufacturer refurbished` 0.176890
## condition.fctrNew 0.947488
## `condition.fctrNew other (see details)` 0.231188
## `condition.fctrSeller refurbished` 0.029748 *
## prdl.my.fctriPad1 0.065329 .
## prdl.my.fctriPad2 0.175875
## prdl.my.fctriPad3 0.911159
## prdl.my.fctriPad4 0.124161
## prdl.my.fctriPadAir 0.068202 .
## prdl.my.fctriPadAir2 0.018868 *
## prdl.my.fctriPadmini 0.146012
## prdl.my.fctriPadmini2 0.133472
## prdl.my.fctriPadmini3 0.518787
## `spdiff.cut.fctr(-100,-10]` 0.000536 ***
## `spdiff.cut.fctr(-10,-1]` 2.05e-09 ***
## `spdiff.cut.fctr(-1,0]` 1.61e-05 ***
## `spdiff.cut.fctr(0,1]` 0.396797
## `spdiff.cut.fctr(1,10]` 1.50e-09 ***
## `spdiff.cut.fctr(10,100]` 1.07e-07 ***
## `spdiff.cut.fctr(100,1e+03]` 0.807911
## biddable 0.000204 ***
## sprice.d20nexp 0.672541
## sprice.log10 0.314768
## sprice.root2 0.108575
## startprice.dcm1.is9 0.264033
## startprice.dcm2.is9 0.781527
## startprice.dgt1.is9 0.829227
## startprice.dgt2.is9 0.841553
## storage.fctr16 0.578023
## storage.fctr32 0.770139
## storage.fctr64 0.870690
## storage.fctrUnknown 0.068914 .
## `cellular.fctr0:carrier.fctrNone` 0.777366
## `cellular.fctr1:carrier.fctrNone` 1.000000
## `cellular.fctrUnknown:carrier.fctrNone` 1.000000
## `cellular.fctr0:carrier.fctrOther` 1.000000
## `cellular.fctr1:carrier.fctrOther` 1.000000
## `cellular.fctrUnknown:carrier.fctrOther` 1.000000
## `cellular.fctr0:carrier.fctrSprint` 1.000000
## `cellular.fctr1:carrier.fctrSprint` 0.176167
## `cellular.fctrUnknown:carrier.fctrSprint` 1.000000
## `cellular.fctr0:carrier.fctrT-Mobile` 1.000000
## `cellular.fctr1:carrier.fctrT-Mobile` 0.790970
## `cellular.fctrUnknown:carrier.fctrT-Mobile` 1.000000
## `cellular.fctr0:carrier.fctrUnknown` 1.000000
## `cellular.fctr1:carrier.fctrUnknown` 0.716765
## `cellular.fctrUnknown:carrier.fctrUnknown` 0.681505
## `cellular.fctr0:carrier.fctrVerizon` 1.000000
## `cellular.fctr1:carrier.fctrVerizon` 0.497135
## `cellular.fctrUnknown:carrier.fctrVerizon` 1.000000
## `prdl.my.fctrUnknown:.clusterid.fctr2` 0.310892
## `prdl.my.fctriPad1:.clusterid.fctr2` 0.564537
## `prdl.my.fctriPad2:.clusterid.fctr2` 1.000000
## `prdl.my.fctriPad3:.clusterid.fctr2` 0.723516
## `prdl.my.fctriPad4:.clusterid.fctr2` 0.405243
## `prdl.my.fctriPadAir:.clusterid.fctr2` 0.873722
## `prdl.my.fctriPadAir2:.clusterid.fctr2` 1.000000
## `prdl.my.fctriPadmini:.clusterid.fctr2` 1.000000
## `prdl.my.fctriPadmini2:.clusterid.fctr2` 0.772109
## `prdl.my.fctriPadmini3:.clusterid.fctr2` 1.000000
## `prdl.my.fctrUnknown:.clusterid.fctr3` 0.653325
## `prdl.my.fctriPad1:.clusterid.fctr3` 0.222707
## `prdl.my.fctriPad2:.clusterid.fctr3` 1.000000
## `prdl.my.fctriPad3:.clusterid.fctr3` 1.000000
## `prdl.my.fctriPad4:.clusterid.fctr3` 0.049619 *
## `prdl.my.fctriPadAir:.clusterid.fctr3` 1.000000
## `prdl.my.fctriPadAir2:.clusterid.fctr3` 1.000000
## `prdl.my.fctriPadmini:.clusterid.fctr3` 1.000000
## `prdl.my.fctriPadmini2:.clusterid.fctr3` 1.000000
## `prdl.my.fctriPadmini3:.clusterid.fctr3` 1.000000
## `spdiff.cut.fctr(-100,-10]:biddable` 0.802944
## `spdiff.cut.fctr(-10,-1]:biddable` 0.561856
## `spdiff.cut.fctr(-1,0]:biddable` 0.577085
## `spdiff.cut.fctr(0,1]:biddable` 0.396797
## `spdiff.cut.fctr(1,10]:biddable` 0.563779
## `spdiff.cut.fctr(10,100]:biddable` 0.070357 .
## `spdiff.cut.fctr(100,1e+03]:biddable` 0.067427 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1405.27 on 1017 degrees of freedom
## Residual deviance: 644.98 on 917 degrees of freedom
## AIC: 846.98
##
## Number of Fisher Scoring iterations: 21
## sold.fctr sold.fctr.predict.CSM.X.bayesglm.N
## 1 N 469
## 2 Y 64
## sold.fctr.predict.CSM.X.bayesglm.Y
## 1 79
## 2 406
## Prediction
## Reference N Y
## N 469 79
## Y 64 406
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.595285e-01 7.180383e-01 8.366530e-01 8.803011e-01 5.383104e-01
## AccuracyPValue McnemarPValue
## 5.250920e-106 2.417038e-01
## sold.fctr sold.fctr.predict.CSM.X.bayesglm.N
## 1 N 313
## 2 Y 59
## sold.fctr.predict.CSM.X.bayesglm.Y
## 1 134
## 2 324
## Prediction
## Reference N Y
## N 313 134
## Y 59 324
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.674699e-01 5.386259e-01 7.372063e-01 7.958201e-01 5.385542e-01
## AccuracyPValue McnemarPValue
## 1.437313e-42 1.000481e-07
## id
## 1 CSM.X.bayesglm
## feats
## 1 spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 5.132 0.401
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8590891 0.9032847 0.8148936 0.9370632
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.8502618 0.8293912
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.836653 0.8803011 0.6549643
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.7902203 0.8389262 0.7415144 0.872051
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.3 0.7705113 0.7674699
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7372063 0.7958201 0.5386259
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.009290129 0.01919755
## label step_major step_minor label_minor bgn end
## 5 fit.models_1_CSM.X 1 4 bayesglm 537.023 545.781
## 6 fit.models_1_CSM.X 1 5 glmnet 545.782 NA
## elapsed
## 5 8.758
## 6 NA
## [1] "fitting model: CSM.X.glmnet"
## [1] " indep_vars: spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.00577 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha
## Length Class Mode
## a0 100 -none- numeric
## beta 10000 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 100 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## 2.397508991
## .rnorm
## -0.013941771
## D.chrs.pnct11.n.log
## 0.402372247
## D.chrs.pnct13.n.log
## -0.049975622
## D.ratio.weight.sum.wrds.n
## -0.055678955
## D.ratio.wrds.stop.n.wrds.n
## -0.033895807
## D.weight.post.stem.sum
## -0.023604659
## D.weight.post.stop.sum
## -0.007405599
## D.weight.sum
## -0.021418370
## D.weight.sum.stem.stop.Ratio
## -1.710221241
## cellular.fctrUnknown
## -0.599622629
## color.fctrGold
## 0.060337617
## color.fctrSpace Gray
## -0.315902340
## color.fctrUnknown
## 0.254898880
## color.fctrWhite
## -0.115711598
## condition.fctrFor parts or not working
## -0.527252513
## condition.fctrManufacturer refurbished
## -1.009203369
## condition.fctrNew
## -0.058478518
## condition.fctrNew other (see details)
## 0.377938778
## condition.fctrSeller refurbished
## -0.861802407
## prdl.my.fctriPad1
## -0.688664048
## prdl.my.fctriPad2
## -0.492075861
## prdl.my.fctriPad4
## 0.668273868
## prdl.my.fctriPadAir
## 0.750021930
## prdl.my.fctriPadAir2
## 1.069321724
## prdl.my.fctriPadmini
## -0.544635434
## prdl.my.fctriPadmini2
## 0.722563719
## prdl.my.fctriPadmini3
## 0.232854343
## spdiff.cut.fctr(-100,-10]
## 0.706646557
## spdiff.cut.fctr(-10,-1]
## 2.444653438
## spdiff.cut.fctr(-1,0]
## 2.719733403
## spdiff.cut.fctr(0,1]
## 1.422671775
## spdiff.cut.fctr(1,10]
## 2.429470647
## spdiff.cut.fctr(10,100]
## 1.811739477
## spdiff.cut.fctr(100,1e+03]
## -1.006053732
## biddable
## 1.063384536
## sprice.d20nexp
## 0.213691735
## sprice.log10
## -0.252197303
## sprice.root2
## -0.116412712
## startprice.dcm1.is9
## -0.311695693
## startprice.dcm2.is9
## -0.107927814
## startprice.dgt2.is9
## 0.068328645
## storage.fctr16
## -0.091582962
## storage.fctr32
## -0.018125285
## storage.fctr64
## 0.196848059
## storage.fctrUnknown
## 1.198928970
## cellular.fctr0:carrier.fctrNone
## 0.178991413
## cellular.fctr1:carrier.fctrSprint
## 0.998012470
## cellular.fctr1:carrier.fctrT-Mobile
## -0.294487639
## cellular.fctr1:carrier.fctrUnknown
## -0.157998492
## cellular.fctrUnknown:carrier.fctrUnknown
## -0.586038538
## cellular.fctr1:carrier.fctrVerizon
## 0.239484343
## prdl.my.fctrUnknown:.clusterid.fctr2
## 0.707410311
## prdl.my.fctriPad1:.clusterid.fctr2
## -0.395000120
## prdl.my.fctriPad3:.clusterid.fctr2
## 0.374540988
## prdl.my.fctriPad4:.clusterid.fctr2
## -0.829707453
## prdl.my.fctriPadmini2:.clusterid.fctr2
## -0.258114409
## prdl.my.fctrUnknown:.clusterid.fctr3
## -0.489073061
## prdl.my.fctriPad1:.clusterid.fctr3
## -0.835718909
## prdl.my.fctriPad4:.clusterid.fctr3
## -2.919265985
## spdiff.cut.fctr(-100,-10]:biddable
## 1.091930842
## spdiff.cut.fctr(-10,-1]:biddable
## 0.563530982
## spdiff.cut.fctr(-1,0]:biddable
## 1.301966062
## spdiff.cut.fctr(0,1]:biddable
## 1.412439379
## spdiff.cut.fctr(1,10]:biddable
## 1.328324045
## spdiff.cut.fctr(10,100]:biddable
## 2.234824100
## spdiff.cut.fctr(100,1e+03]:biddable
## 3.295167237
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## 2.4732516702
## .rnorm
## -0.0147945637
## D.chrs.pnct11.n.log
## 0.4063033361
## D.chrs.pnct13.n.log
## -0.0556546371
## D.ratio.weight.sum.wrds.n
## -0.0568393760
## D.ratio.wrds.stop.n.wrds.n
## -0.0679430265
## D.weight.post.stem.sum
## -0.0263036188
## D.weight.post.stop.sum
## -0.0077854736
## D.weight.sum
## -0.0227810767
## D.weight.sum.stem.stop.Ratio
## -1.7346449856
## D.wrds.n.log
## 0.0005928951
## cellular.fctrUnknown
## -0.6177984624
## color.fctrGold
## 0.0683620735
## color.fctrSpace Gray
## -0.3208405212
## color.fctrUnknown
## 0.2608347041
## color.fctrWhite
## -0.1193992178
## condition.fctrFor parts or not working
## -0.5461930702
## condition.fctrManufacturer refurbished
## -1.0285817470
## condition.fctrNew
## -0.0514396465
## condition.fctrNew other (see details)
## 0.3929190928
## condition.fctrSeller refurbished
## -0.8791499570
## prdl.my.fctriPad1
## -0.7115699626
## prdl.my.fctriPad2
## -0.5020786040
## prdl.my.fctriPad4
## 0.7009602312
## prdl.my.fctriPadAir
## 0.7793261251
## prdl.my.fctriPadAir2
## 1.1046915404
## prdl.my.fctriPadmini
## -0.5514123263
## prdl.my.fctriPadmini2
## 0.7467350290
## prdl.my.fctriPadmini3
## 0.2560292192
## spdiff.cut.fctr(-100,-10]
## 0.7538619841
## spdiff.cut.fctr(-10,-1]
## 2.5040502365
## spdiff.cut.fctr(-1,0]
## 2.7959985877
## spdiff.cut.fctr(0,1]
## 1.4499859089
## spdiff.cut.fctr(1,10]
## 2.4872071703
## spdiff.cut.fctr(10,100]
## 1.8606220547
## spdiff.cut.fctr(100,1e+03]
## -1.0018932838
## biddable
## 1.0843737704
## sprice.d20nexp
## 0.1747363583
## sprice.log10
## -0.2569500814
## sprice.root2
## -0.1193024617
## startprice.dcm1.is9
## -0.3154287297
## startprice.dcm2.is9
## -0.1108138174
## startprice.dgt2.is9
## 0.0719395701
## storage.fctr16
## -0.0998591473
## storage.fctr32
## -0.0212335197
## storage.fctr64
## 0.1992001047
## storage.fctrUnknown
## 1.2362752586
## cellular.fctr0:carrier.fctrNone
## 0.1793958687
## cellular.fctr1:carrier.fctrSprint
## 1.0128090093
## cellular.fctr1:carrier.fctrT-Mobile
## -0.2981699178
## cellular.fctr1:carrier.fctrUnknown
## -0.1606971820
## cellular.fctrUnknown:carrier.fctrUnknown
## -0.6033484544
## cellular.fctr1:carrier.fctrVerizon
## 0.2452288435
## prdl.my.fctrUnknown:.clusterid.fctr2
## 0.7269935317
## prdl.my.fctriPad1:.clusterid.fctr2
## -0.4015491508
## prdl.my.fctriPad3:.clusterid.fctr2
## 0.3757808061
## prdl.my.fctriPad4:.clusterid.fctr2
## -0.8651709167
## prdl.my.fctriPadAir:.clusterid.fctr2
## -0.0082995340
## prdl.my.fctriPadmini2:.clusterid.fctr2
## -0.2730368764
## prdl.my.fctrUnknown:.clusterid.fctr3
## -0.5025103830
## prdl.my.fctriPad1:.clusterid.fctr3
## -0.8506771512
## prdl.my.fctriPad4:.clusterid.fctr3
## -3.0253180284
## spdiff.cut.fctr(-100,-10]:biddable
## 1.0757666026
## spdiff.cut.fctr(-10,-1]:biddable
## 0.5372859262
## spdiff.cut.fctr(-1,0]:biddable
## 1.3329022121
## spdiff.cut.fctr(0,1]:biddable
## 1.4379783926
## spdiff.cut.fctr(1,10]:biddable
## 1.3228314445
## spdiff.cut.fctr(10,100]:biddable
## 2.2452142403
## spdiff.cut.fctr(100,1e+03]:biddable
## 3.3176415949
## sold.fctr sold.fctr.predict.CSM.X.glmnet.N
## 1 N 471
## 2 Y 67
## sold.fctr.predict.CSM.X.glmnet.Y
## 1 77
## 2 403
## Prediction
## Reference N Y
## N 471 77
## Y 67 403
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.585462e-01 7.158519e-01 8.356115e-01 8.793838e-01 5.383104e-01
## AccuracyPValue McnemarPValue
## 2.741749e-105 4.532547e-01
## sold.fctr sold.fctr.predict.CSM.X.glmnet.N
## 1 N 348
## 2 Y 79
## sold.fctr.predict.CSM.X.glmnet.Y
## 1 99
## 2 304
## Prediction
## Reference N Y
## N 348 99
## Y 79 304
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.855422e-01 5.701259e-01 7.560267e-01 8.130134e-01 5.385542e-01
## AccuracyPValue McnemarPValue
## 1.094538e-49 1.544146e-01
## id
## 1 CSM.X.glmnet
## feats
## 1 spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 14.824 0.552
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.854531 0.9069343 0.8021277 0.9345318
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.8484211 0.8323381
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8356115 0.8793838 0.6607067
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.7937629 0.8434004 0.7441253 0.8713734
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.4 0.7735369 0.7855422
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7560267 0.8130134 0.5701259
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01308189 0.02669142
## label step_major step_minor label_minor bgn end
## 6 fit.models_1_CSM.X 1 5 glmnet 545.782 565.089
## 7 fit.models_1_CSM.X 1 6 nnet 565.090 NA
## elapsed
## 6 19.307
## 7 NA
## [1] "fitting model: CSM.X.nnet"
## [1] " indep_vars: spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr"## Loading required package: nnet
## Aggregating results
## Selecting tuning parameters
## Fitting size = 1, decay = 0.1 on full training set
## # weights: 103
## initial value 724.965306
## iter 10 value 516.340099
## iter 20 value 440.806558
## iter 30 value 384.133446
## iter 40 value 358.725925
## iter 50 value 348.164595
## iter 60 value 337.066089
## iter 70 value 331.800295
## iter 80 value 330.382781
## iter 90 value 330.219842
## iter 100 value 330.215988
## final value 330.215988
## stopped after 100 iterations## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: size## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: decay## Loading required package: NeuralNetTools## Warning: replacing previous import by 'scales::alpha' when loading
## 'NeuralNetTools'
## a 100-1-1 network with 103 weights
## options were - entropy fitting decay=0.1
## b->h1 i1->h1 i2->h1 i3->h1 i4->h1 i5->h1 i6->h1 i7->h1
## -1.21 0.00 1.10 -0.31 0.03 -0.75 -0.02 1.85
## i8->h1 i9->h1 i10->h1 i11->h1 i12->h1 i13->h1 i14->h1 i15->h1
## -0.34 2.26 0.29 -0.47 0.29 -1.49 -1.73 0.06
## i16->h1 i17->h1 i18->h1 i19->h1 i20->h1 i21->h1 i22->h1 i23->h1
## -0.34 -0.63 0.14 -0.16 0.17 -0.20 0.00 0.58
## i24->h1 i25->h1 i26->h1 i27->h1 i28->h1 i29->h1 i30->h1 i31->h1
## 0.74 -0.10 -0.41 0.53 0.57 0.28 -0.15 -0.79
## i32->h1 i33->h1 i34->h1 i35->h1 i36->h1 i37->h1 i38->h1 i39->h1
## -0.83 -1.02 0.23 -0.87 -0.45 -1.37 -2.37 -2.53
## i40->h1 i41->h1 i42->h1 i43->h1 i44->h1 i45->h1 i46->h1 i47->h1
## -1.05 -2.33 -1.96 0.24 -1.42 0.38 0.51 0.06
## i48->h1 i49->h1 i50->h1 i51->h1 i52->h1 i53->h1 i54->h1 i55->h1
## 0.20 0.05 -0.05 -0.03 0.24 0.14 0.01 -0.87
## i56->h1 i57->h1 i58->h1 i59->h1 i60->h1 i61->h1 i62->h1 i63->h1
## -0.73 0.00 0.00 0.00 0.00 0.00 0.00 -0.56
## i64->h1 i65->h1 i66->h1 i67->h1 i68->h1 i69->h1 i70->h1 i71->h1
## 0.00 0.00 0.16 0.00 0.00 0.25 0.14 0.00
## i72->h1 i73->h1 i74->h1 i75->h1 i76->h1 i77->h1 i78->h1 i79->h1
## -0.21 0.00 -0.63 0.33 0.00 -0.12 0.75 0.06
## i80->h1 i81->h1 i82->h1 i83->h1 i84->h1 i85->h1 i86->h1 i87->h1
## 0.00 0.00 -0.15 0.00 0.02 0.56 0.00 0.00
## i88->h1 i89->h1 i90->h1 i91->h1 i92->h1 i93->h1 i94->h1 i95->h1
## 1.92 0.00 0.00 0.00 0.00 0.00 0.35 0.77
## i96->h1 i97->h1 i98->h1 i99->h1 i100->h1
## -0.50 -1.05 -0.58 -1.29 -2.14
## b->o h1->o
## 3.92 -8.69
## sold.fctr sold.fctr.predict.CSM.X.nnet.N sold.fctr.predict.CSM.X.nnet.Y
## 1 N 469 79
## 2 Y 61 409
## Prediction
## Reference N Y
## N 469 79
## Y 61 409
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.624754e-01 7.240786e-01 8.397793e-01 8.830508e-01 5.383104e-01
## AccuracyPValue McnemarPValue
## 3.512861e-108 1.507856e-01
## sold.fctr sold.fctr.predict.CSM.X.nnet.N sold.fctr.predict.CSM.X.nnet.Y
## 1 N 371 76
## 2 Y 97 286
## Prediction
## Reference N Y
## N 371 76
## Y 97 286
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.915663e-01 5.789866e-01 7.623176e-01 8.187270e-01 5.385542e-01
## AccuracyPValue McnemarPValue
## 3.265805e-52 1.283673e-01
## id
## 1 CSM.X.nnet
## feats
## 1 spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 37.288 0.216
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8654721 0.9032847 0.8276596 0.939583
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.8538622 0.8267681
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8397793 0.8830508 0.6499515
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.788357 0.8299776 0.7467363 0.8693232
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.7677852 0.7915663
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7623176 0.818727 0.5789866
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01204383 0.0245082
## label step_major step_minor label_minor bgn end
## 7 fit.models_1_CSM.X 1 6 nnet 565.090 609.263
## 8 fit.models_1_CSM.X 1 7 rpart 609.263 NA
## elapsed
## 7 44.173
## 8 NA
## [1] "fitting model: CSM.X.rpart"
## [1] " indep_vars: spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr"
## + Fold1.Rep1: cp=0.008511
## - Fold1.Rep1: cp=0.008511
## + Fold2.Rep1: cp=0.008511
## - Fold2.Rep1: cp=0.008511
## + Fold3.Rep1: cp=0.008511
## - Fold3.Rep1: cp=0.008511
## + Fold1.Rep2: cp=0.008511
## - Fold1.Rep2: cp=0.008511
## + Fold2.Rep2: cp=0.008511
## - Fold2.Rep2: cp=0.008511
## + Fold3.Rep2: cp=0.008511
## - Fold3.Rep2: cp=0.008511
## + Fold1.Rep3: cp=0.008511
## - Fold1.Rep3: cp=0.008511
## + Fold2.Rep3: cp=0.008511
## - Fold2.Rep3: cp=0.008511
## + Fold3.Rep3: cp=0.008511
## - Fold3.Rep3: cp=0.008511
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.0128 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 1018
##
## CP nsplit rel error
## 1 0.54680851 0 1.0000000
## 2 0.03829787 1 0.4531915
## 3 0.01276596 3 0.3765957
##
## Variable importance
## biddable sprice.d20nexp
## 26 19
## sprice.log10 sprice.root2
## 19 19
## spdiff.cut.fctr(10,100]:biddable spdiff.cut.fctr(-100,-10]:biddable
## 7 6
## spdiff.cut.fctr(10,100] prdl.my.fctriPadAir2
## 2 1
## condition.fctrNew
## 1
##
## Node number 1: 1018 observations, complexity param=0.5468085
## predicted class=N expected loss=0.4616896 P(node) =1
## class counts: 548 470
## probabilities: 0.538 0.462
## left son=2 (553 obs) right son=3 (465 obs)
## Primary splits:
## biddable < 0.5 to the left, improve=169.50150, (0 missing)
## sprice.d20nexp < 0.005287171 to the left, improve=133.60310, (0 missing)
## sprice.log10 < 4.65253 to the right, improve=133.60310, (0 missing)
## sprice.root2 < 10.23963 to the right, improve=133.60310, (0 missing)
## spdiff.cut.fctr(10,100]:biddable < 0.5 to the left, improve= 46.43072, (0 missing)
## Surrogate splits:
## sprice.d20nexp < 0.0004767525 to the left, agree=0.752, adj=0.458, (0 split)
## sprice.log10 < 5.013527 to the right, agree=0.752, adj=0.458, (0 split)
## sprice.root2 < 12.26518 to the right, agree=0.752, adj=0.458, (0 split)
## spdiff.cut.fctr(-100,-10]:biddable < 0.5 to the left, agree=0.643, adj=0.219, (0 split)
## spdiff.cut.fctr(10,100]:biddable < 0.5 to the left, agree=0.623, adj=0.174, (0 split)
##
## Node number 2: 553 observations
## predicted class=N expected loss=0.1971067 P(node) =0.543222
## class counts: 444 109
## probabilities: 0.803 0.197
##
## Node number 3: 465 observations, complexity param=0.03829787
## predicted class=Y expected loss=0.2236559 P(node) =0.456778
## class counts: 104 361
## probabilities: 0.224 0.776
## left son=6 (163 obs) right son=7 (302 obs)
## Primary splits:
## sprice.log10 < 4.941607 to the right, improve=50.193340, (0 missing)
## sprice.root2 < 11.83195 to the right, improve=50.193340, (0 missing)
## sprice.d20nexp < 0.00091211 to the left, improve=50.193340, (0 missing)
## spdiff.cut.fctr(10,100] < 0.5 to the left, improve= 7.765835, (0 missing)
## spdiff.cut.fctr(10,100]:biddable < 0.5 to the left, improve= 7.765835, (0 missing)
## Surrogate splits:
## sprice.d20nexp < 0.00091211 to the left, agree=1.000, adj=1.000, (0 split)
## sprice.root2 < 11.83195 to the right, agree=1.000, adj=1.000, (0 split)
## prdl.my.fctriPadAir2 < 0.5 to the right, agree=0.688, adj=0.110, (0 split)
## condition.fctrNew < 0.5 to the right, agree=0.686, adj=0.104, (0 split)
## prdl.my.fctriPadAir < 0.5 to the right, agree=0.671, adj=0.061, (0 split)
##
## Node number 6: 163 observations, complexity param=0.03829787
## predicted class=N expected loss=0.4601227 P(node) =0.1601179
## class counts: 88 75
## probabilities: 0.540 0.460
## left son=12 (138 obs) right son=13 (25 obs)
## Primary splits:
## spdiff.cut.fctr(10,100] < 0.5 to the left, improve=14.757250, (0 missing)
## spdiff.cut.fctr(10,100]:biddable < 0.5 to the left, improve=14.757250, (0 missing)
## prdl.my.fctriPad3 < 0.5 to the left, improve= 3.293730, (0 missing)
## prdl.my.fctriPadAir2 < 0.5 to the left, improve= 3.226569, (0 missing)
## prdl.my.fctriPad2 < 0.5 to the right, improve= 3.083225, (0 missing)
## Surrogate splits:
## spdiff.cut.fctr(10,100]:biddable < 0.5 to the left, agree=1, adj=1, (0 split)
##
## Node number 7: 302 observations
## predicted class=Y expected loss=0.05298013 P(node) =0.2966601
## class counts: 16 286
## probabilities: 0.053 0.947
##
## Node number 12: 138 observations
## predicted class=N expected loss=0.3695652 P(node) =0.1355599
## class counts: 87 51
## probabilities: 0.630 0.370
##
## Node number 13: 25 observations
## predicted class=Y expected loss=0.04 P(node) =0.02455796
## class counts: 1 24
## probabilities: 0.040 0.960
##
## n= 1018
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 1018 470 N (0.53831041 0.46168959)
## 2) biddable< 0.5 553 109 N (0.80289331 0.19710669) *
## 3) biddable>=0.5 465 104 Y (0.22365591 0.77634409)
## 6) sprice.log10>=4.941607 163 75 N (0.53987730 0.46012270)
## 12) spdiff.cut.fctr(10,100]< 0.5 138 51 N (0.63043478 0.36956522) *
## 13) spdiff.cut.fctr(10,100]>=0.5 25 1 Y (0.04000000 0.96000000) *
## 7) sprice.log10< 4.941607 302 16 Y (0.05298013 0.94701987) *
## sold.fctr sold.fctr.predict.CSM.X.rpart.N
## 1 N 531
## 2 Y 160
## sold.fctr.predict.CSM.X.rpart.Y
## 1 17
## 2 310
## Prediction
## Reference N Y
## N 531 17
## Y 160 310
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.261297e-01 6.424640e-01 8.014236e-01 8.489327e-01 5.383104e-01
## AccuracyPValue McnemarPValue
## 1.866627e-83 1.356115e-26
## sold.fctr sold.fctr.predict.CSM.X.rpart.N
## 1 N 354
## 2 Y 107
## sold.fctr.predict.CSM.X.rpart.Y
## 1 93
## 2 276
## Prediction
## Reference N Y
## N 354 93
## Y 107 276
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.590361e-01 5.139178e-01 7.284488e-01 7.877710e-01 5.385542e-01
## AccuracyPValue McnemarPValue
## 1.789179e-39 3.579707e-01
## id
## 1 CSM.X.rpart
## feats
## 1 spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 5 3.431 0.081
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8142763 0.9689781 0.6595745 0.8400159
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.9 0.7779172 0.8251457
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8014236 0.8489327 0.6414684
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.7534652 0.9194631 0.5874674 0.7890637
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.3 0.7340426 0.7590361
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7284488 0.787771 0.5139178
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01198064 0.0244548
## label step_major step_minor label_minor bgn end
## 8 fit.models_1_CSM.X 1 7 rpart 609.263 617.084
## 9 fit.models_1_CSM.X 1 8 gbm 617.085 NA
## elapsed
## 8 7.822
## 9 NA
## [1] "fitting model: CSM.X.gbm"
## [1] " indep_vars: spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr"## Loading required package: gbm
## Loading required package: splines
## Loaded gbm 2.1.1## Aggregating results
## Selecting tuning parameters
## Fitting n.trees = 100, interaction.depth = 5, shrinkage = 0.1, n.minobsinnode = 10 on full training set## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 56: cellular.fctr1:carrier.fctrNone has no variation.## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 57: cellular.fctrUnknown:carrier.fctrNone has no variation.## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 58: cellular.fctr0:carrier.fctrOther has no variation.## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 59: cellular.fctr1:carrier.fctrOther has no variation.## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 60: cellular.fctrUnknown:carrier.fctrOther has no variation.## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 61: cellular.fctr0:carrier.fctrSprint has no variation.## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 63: cellular.fctrUnknown:carrier.fctrSprint has no variation.## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 64: cellular.fctr0:carrier.fctrT-Mobile has no variation.## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 66: cellular.fctrUnknown:carrier.fctrT-Mobile has no variation.## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 67: cellular.fctr0:carrier.fctrUnknown has no variation.## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 70: cellular.fctr0:carrier.fctrVerizon has no variation.## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 72: cellular.fctrUnknown:carrier.fctrVerizon has no variation.## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 75: prdl.my.fctriPad2:.clusterid.fctr2 has no variation.## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 79: prdl.my.fctriPadAir2:.clusterid.fctr2 has no variation.## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 80: prdl.my.fctriPadmini:.clusterid.fctr2 has no variation.## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 82: prdl.my.fctriPadmini3:.clusterid.fctr2 has no variation.## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 85: prdl.my.fctriPad2:.clusterid.fctr3 has no variation.## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 86: prdl.my.fctriPad3:.clusterid.fctr3 has no variation.## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 88: prdl.my.fctriPadAir:.clusterid.fctr3 has no variation.## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 89: prdl.my.fctriPadAir2:.clusterid.fctr3 has no variation.## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 90: prdl.my.fctriPadmini:.clusterid.fctr3 has no variation.## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 91: prdl.my.fctriPadmini2:.clusterid.fctr3 has no variation.## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 92: prdl.my.fctriPadmini3:.clusterid.fctr3 has no variation.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2859 nan 0.1000 0.0458
## 2 1.2127 nan 0.1000 0.0339
## 3 1.1469 nan 0.1000 0.0322
## 4 1.0992 nan 0.1000 0.0241
## 5 1.0534 nan 0.1000 0.0214
## 6 1.0141 nan 0.1000 0.0188
## 7 0.9818 nan 0.1000 0.0163
## 8 0.9516 nan 0.1000 0.0133
## 9 0.9276 nan 0.1000 0.0102
## 10 0.9037 nan 0.1000 0.0119
## 20 0.7732 nan 0.1000 0.0033
## 40 0.6662 nan 0.1000 -0.0008
## 60 0.5981 nan 0.1000 -0.0008
## 80 0.5556 nan 0.1000 -0.0007
## 100 0.5136 nan 0.1000 -0.0012## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: interaction.depth## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: shrinkage## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: n.minobsinnode
## var
## biddable biddable
## sprice.log10 sprice.log10
## sprice.d20nexp sprice.d20nexp
## spdiff.cut.fctr(10,100] spdiff.cut.fctr(10,100]
## spdiff.cut.fctr(1,10] spdiff.cut.fctr(1,10]
## D.ratio.weight.sum.wrds.n D.ratio.weight.sum.wrds.n
## D.weight.sum.stem.stop.Ratio D.weight.sum.stem.stop.Ratio
## spdiff.cut.fctr(-10,-1] spdiff.cut.fctr(-10,-1]
## prdl.my.fctriPadAir2 prdl.my.fctriPadAir2
## D.weight.post.stem.sum D.weight.post.stem.sum
## spdiff.cut.fctr(-100,-10] spdiff.cut.fctr(-100,-10]
## D.weight.post.stop.sum D.weight.post.stop.sum
## startprice.dgt1.is9 startprice.dgt1.is9
## D.ratio.wrds.stop.n.wrds.n D.ratio.wrds.stop.n.wrds.n
## spdiff.cut.fctr(-100,-10]:biddable spdiff.cut.fctr(-100,-10]:biddable
## prdl.my.fctriPadmini prdl.my.fctriPadmini
## D.wrds.n.log D.wrds.n.log
## spdiff.cut.fctr(100,1e+03] spdiff.cut.fctr(100,1e+03]
## D.chrs.pnct11.n.log D.chrs.pnct11.n.log
## prdl.my.fctriPad2 prdl.my.fctriPad2
## D.chrs.uppr.n.log D.chrs.uppr.n.log
## spdiff.cut.fctr(10,100]:biddable spdiff.cut.fctr(10,100]:biddable
## startprice.dcm1.is9 startprice.dcm1.is9
## prdl.my.fctriPad1 prdl.my.fctriPad1
## spdiff.cut.fctr(-1,0] spdiff.cut.fctr(-1,0]
## prdl.my.fctriPadmini2 prdl.my.fctriPadmini2
## D.terms.post.stem.n.log D.terms.post.stem.n.log
## condition.fctrFor parts or not working condition.fctrFor parts or not working
## cellular.fctr0:carrier.fctrNone cellular.fctr0:carrier.fctrNone
## prdl.my.fctriPad3 prdl.my.fctriPad3
## startprice.dcm2.is9 startprice.dcm2.is9
## D.chrs.n.log D.chrs.n.log
## color.fctrWhite color.fctrWhite
## color.fctrUnknown color.fctrUnknown
## spdiff.cut.fctr(-10,-1]:biddable spdiff.cut.fctr(-10,-1]:biddable
## prdl.my.fctriPadAir prdl.my.fctriPadAir
## storage.fctrUnknown storage.fctrUnknown
## cellular.fctrUnknown cellular.fctrUnknown
## storage.fctr64 storage.fctr64
## condition.fctrNew condition.fctrNew
## D.chrs.pnct13.n.log D.chrs.pnct13.n.log
## color.fctrSpace Gray color.fctrSpace Gray
## cellular.fctr1:carrier.fctrVerizon cellular.fctr1:carrier.fctrVerizon
## D.wrds.stop.n.log D.wrds.stop.n.log
## storage.fctr32 storage.fctr32
## startprice.dgt2.is9 startprice.dgt2.is9
## D.terms.post.stop.n.log D.terms.post.stop.n.log
## storage.fctr16 storage.fctr16
## condition.fctrManufacturer refurbished condition.fctrManufacturer refurbished
## cellular.fctr1 cellular.fctr1
## prdl.my.fctriPadmini3 prdl.my.fctriPadmini3
## cellular.fctr1:carrier.fctrSprint cellular.fctr1:carrier.fctrSprint
## prdl.my.fctriPad1:.clusterid.fctr2 prdl.my.fctriPad1:.clusterid.fctr2
## prdl.my.fctriPad4 prdl.my.fctriPad4
## condition.fctrSeller refurbished condition.fctrSeller refurbished
## condition.fctrNew other (see details) condition.fctrNew other (see details)
## D.weight.sum D.weight.sum
## D.wrds.unq.n.log D.wrds.unq.n.log
## color.fctrGold color.fctrGold
## spdiff.cut.fctr(0,1] spdiff.cut.fctr(0,1]
## sprice.root2 sprice.root2
## cellular.fctr1:carrier.fctrNone cellular.fctr1:carrier.fctrNone
## cellular.fctrUnknown:carrier.fctrNone cellular.fctrUnknown:carrier.fctrNone
## cellular.fctr0:carrier.fctrOther cellular.fctr0:carrier.fctrOther
## cellular.fctr1:carrier.fctrOther cellular.fctr1:carrier.fctrOther
## cellular.fctrUnknown:carrier.fctrOther cellular.fctrUnknown:carrier.fctrOther
## cellular.fctr0:carrier.fctrSprint cellular.fctr0:carrier.fctrSprint
## cellular.fctrUnknown:carrier.fctrSprint cellular.fctrUnknown:carrier.fctrSprint
## cellular.fctr0:carrier.fctrT-Mobile cellular.fctr0:carrier.fctrT-Mobile
## cellular.fctr1:carrier.fctrT-Mobile cellular.fctr1:carrier.fctrT-Mobile
## cellular.fctrUnknown:carrier.fctrT-Mobile cellular.fctrUnknown:carrier.fctrT-Mobile
## cellular.fctr0:carrier.fctrUnknown cellular.fctr0:carrier.fctrUnknown
## cellular.fctr1:carrier.fctrUnknown cellular.fctr1:carrier.fctrUnknown
## cellular.fctrUnknown:carrier.fctrUnknown cellular.fctrUnknown:carrier.fctrUnknown
## cellular.fctr0:carrier.fctrVerizon cellular.fctr0:carrier.fctrVerizon
## cellular.fctrUnknown:carrier.fctrVerizon cellular.fctrUnknown:carrier.fctrVerizon
## prdl.my.fctrUnknown:.clusterid.fctr2 prdl.my.fctrUnknown:.clusterid.fctr2
## prdl.my.fctriPad2:.clusterid.fctr2 prdl.my.fctriPad2:.clusterid.fctr2
## prdl.my.fctriPad3:.clusterid.fctr2 prdl.my.fctriPad3:.clusterid.fctr2
## prdl.my.fctriPad4:.clusterid.fctr2 prdl.my.fctriPad4:.clusterid.fctr2
## prdl.my.fctriPadAir:.clusterid.fctr2 prdl.my.fctriPadAir:.clusterid.fctr2
## prdl.my.fctriPadAir2:.clusterid.fctr2 prdl.my.fctriPadAir2:.clusterid.fctr2
## prdl.my.fctriPadmini:.clusterid.fctr2 prdl.my.fctriPadmini:.clusterid.fctr2
## prdl.my.fctriPadmini2:.clusterid.fctr2 prdl.my.fctriPadmini2:.clusterid.fctr2
## prdl.my.fctriPadmini3:.clusterid.fctr2 prdl.my.fctriPadmini3:.clusterid.fctr2
## prdl.my.fctrUnknown:.clusterid.fctr3 prdl.my.fctrUnknown:.clusterid.fctr3
## prdl.my.fctriPad1:.clusterid.fctr3 prdl.my.fctriPad1:.clusterid.fctr3
## prdl.my.fctriPad2:.clusterid.fctr3 prdl.my.fctriPad2:.clusterid.fctr3
## prdl.my.fctriPad3:.clusterid.fctr3 prdl.my.fctriPad3:.clusterid.fctr3
## prdl.my.fctriPad4:.clusterid.fctr3 prdl.my.fctriPad4:.clusterid.fctr3
## prdl.my.fctriPadAir:.clusterid.fctr3 prdl.my.fctriPadAir:.clusterid.fctr3
## prdl.my.fctriPadAir2:.clusterid.fctr3 prdl.my.fctriPadAir2:.clusterid.fctr3
## prdl.my.fctriPadmini:.clusterid.fctr3 prdl.my.fctriPadmini:.clusterid.fctr3
## prdl.my.fctriPadmini2:.clusterid.fctr3 prdl.my.fctriPadmini2:.clusterid.fctr3
## prdl.my.fctriPadmini3:.clusterid.fctr3 prdl.my.fctriPadmini3:.clusterid.fctr3
## spdiff.cut.fctr(-1,0]:biddable spdiff.cut.fctr(-1,0]:biddable
## spdiff.cut.fctr(0,1]:biddable spdiff.cut.fctr(0,1]:biddable
## spdiff.cut.fctr(1,10]:biddable spdiff.cut.fctr(1,10]:biddable
## spdiff.cut.fctr(100,1e+03]:biddable spdiff.cut.fctr(100,1e+03]:biddable
## rel.inf
## biddable 32.14304083
## sprice.log10 13.00656469
## sprice.d20nexp 11.96411850
## spdiff.cut.fctr(10,100] 5.48768923
## spdiff.cut.fctr(1,10] 2.83342765
## D.ratio.weight.sum.wrds.n 2.67098917
## D.weight.sum.stem.stop.Ratio 2.09416431
## spdiff.cut.fctr(-10,-1] 2.06478301
## prdl.my.fctriPadAir2 1.99468929
## D.weight.post.stem.sum 1.82377361
## spdiff.cut.fctr(-100,-10] 1.68926656
## D.weight.post.stop.sum 1.65515352
## startprice.dgt1.is9 1.33165278
## D.ratio.wrds.stop.n.wrds.n 1.27463822
## spdiff.cut.fctr(-100,-10]:biddable 0.96023476
## prdl.my.fctriPadmini 0.91766515
## D.wrds.n.log 0.90071073
## spdiff.cut.fctr(100,1e+03] 0.89340145
## D.chrs.pnct11.n.log 0.87263117
## prdl.my.fctriPad2 0.86791355
## D.chrs.uppr.n.log 0.78279292
## spdiff.cut.fctr(10,100]:biddable 0.76579222
## startprice.dcm1.is9 0.73780552
## prdl.my.fctriPad1 0.70267459
## spdiff.cut.fctr(-1,0] 0.63627310
## prdl.my.fctriPadmini2 0.58645306
## D.terms.post.stem.n.log 0.57347438
## condition.fctrFor parts or not working 0.57130731
## cellular.fctr0:carrier.fctrNone 0.56989431
## prdl.my.fctriPad3 0.55286481
## startprice.dcm2.is9 0.50510414
## D.chrs.n.log 0.46197133
## color.fctrWhite 0.46002488
## color.fctrUnknown 0.44543576
## spdiff.cut.fctr(-10,-1]:biddable 0.36515555
## prdl.my.fctriPadAir 0.35389756
## storage.fctrUnknown 0.31229550
## cellular.fctrUnknown 0.30046252
## storage.fctr64 0.29398607
## condition.fctrNew 0.27863960
## D.chrs.pnct13.n.log 0.27373166
## color.fctrSpace Gray 0.23784317
## cellular.fctr1:carrier.fctrVerizon 0.22176957
## D.wrds.stop.n.log 0.19347620
## storage.fctr32 0.18933659
## startprice.dgt2.is9 0.17229630
## D.terms.post.stop.n.log 0.16958460
## storage.fctr16 0.15879311
## condition.fctrManufacturer refurbished 0.15812122
## cellular.fctr1 0.11479510
## prdl.my.fctriPadmini3 0.08909664
## cellular.fctr1:carrier.fctrSprint 0.08693383
## prdl.my.fctriPad1:.clusterid.fctr2 0.08625362
## prdl.my.fctriPad4 0.06041165
## condition.fctrSeller refurbished 0.04802603
## condition.fctrNew other (see details) 0.03671745
## D.weight.sum 0.00000000
## D.wrds.unq.n.log 0.00000000
## color.fctrGold 0.00000000
## spdiff.cut.fctr(0,1] 0.00000000
## sprice.root2 0.00000000
## cellular.fctr1:carrier.fctrNone 0.00000000
## cellular.fctrUnknown:carrier.fctrNone 0.00000000
## cellular.fctr0:carrier.fctrOther 0.00000000
## cellular.fctr1:carrier.fctrOther 0.00000000
## cellular.fctrUnknown:carrier.fctrOther 0.00000000
## cellular.fctr0:carrier.fctrSprint 0.00000000
## cellular.fctrUnknown:carrier.fctrSprint 0.00000000
## cellular.fctr0:carrier.fctrT-Mobile 0.00000000
## cellular.fctr1:carrier.fctrT-Mobile 0.00000000
## cellular.fctrUnknown:carrier.fctrT-Mobile 0.00000000
## cellular.fctr0:carrier.fctrUnknown 0.00000000
## cellular.fctr1:carrier.fctrUnknown 0.00000000
## cellular.fctrUnknown:carrier.fctrUnknown 0.00000000
## cellular.fctr0:carrier.fctrVerizon 0.00000000
## cellular.fctrUnknown:carrier.fctrVerizon 0.00000000
## prdl.my.fctrUnknown:.clusterid.fctr2 0.00000000
## prdl.my.fctriPad2:.clusterid.fctr2 0.00000000
## prdl.my.fctriPad3:.clusterid.fctr2 0.00000000
## prdl.my.fctriPad4:.clusterid.fctr2 0.00000000
## prdl.my.fctriPadAir:.clusterid.fctr2 0.00000000
## prdl.my.fctriPadAir2:.clusterid.fctr2 0.00000000
## prdl.my.fctriPadmini:.clusterid.fctr2 0.00000000
## prdl.my.fctriPadmini2:.clusterid.fctr2 0.00000000
## prdl.my.fctriPadmini3:.clusterid.fctr2 0.00000000
## prdl.my.fctrUnknown:.clusterid.fctr3 0.00000000
## prdl.my.fctriPad1:.clusterid.fctr3 0.00000000
## prdl.my.fctriPad2:.clusterid.fctr3 0.00000000
## prdl.my.fctriPad3:.clusterid.fctr3 0.00000000
## prdl.my.fctriPad4:.clusterid.fctr3 0.00000000
## prdl.my.fctriPadAir:.clusterid.fctr3 0.00000000
## prdl.my.fctriPadAir2:.clusterid.fctr3 0.00000000
## prdl.my.fctriPadmini:.clusterid.fctr3 0.00000000
## prdl.my.fctriPadmini2:.clusterid.fctr3 0.00000000
## prdl.my.fctriPadmini3:.clusterid.fctr3 0.00000000
## spdiff.cut.fctr(-1,0]:biddable 0.00000000
## spdiff.cut.fctr(0,1]:biddable 0.00000000
## spdiff.cut.fctr(1,10]:biddable 0.00000000
## spdiff.cut.fctr(100,1e+03]:biddable 0.00000000
## sold.fctr sold.fctr.predict.CSM.X.gbm.N sold.fctr.predict.CSM.X.gbm.Y
## 1 N 497 51
## 2 Y 35 435
## Prediction
## Reference N Y
## N 497 51
## Y 35 435
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.155206e-01 8.304542e-01 8.967192e-01 9.318742e-01 5.383104e-01
## AccuracyPValue McnemarPValue
## 1.524407e-153 1.057722e-01
## sold.fctr sold.fctr.predict.CSM.X.gbm.N sold.fctr.predict.CSM.X.gbm.Y
## 1 N 322 125
## 2 Y 66 317
## Prediction
## Reference N Y
## N 322 125
## Y 66 317
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.698795e-01 5.420564e-01 7.397113e-01 7.981170e-01 5.385542e-01
## AccuracyPValue McnemarPValue
## 1.766677e-43 2.707932e-05
## id
## 1 CSM.X.gbm
## feats
## 1 spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 12.319 1.007
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8910157 0.9416058 0.8404255 0.9666078
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.9100418 0.8323391
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8967192 0.9318742 0.6607657
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.7573963 0.8411633 0.6736292 0.8482836
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.3 0.7684848 0.7698795
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7397113 0.798117 0.5420564
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01727058 0.03505153
## label step_major step_minor label_minor bgn end
## 9 fit.models_1_CSM.X 1 8 gbm 617.085 634.001
## 10 fit.models_1_CSM.X 1 9 avNNet 634.001 NA
## elapsed
## 9 16.916
## 10 NA
## [1] "fitting model: CSM.X.avNNet"
## [1] " indep_vars: spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting size = 1, decay = 0.01, bag = FALSE on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: size## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: bag
## Length Class Mode
## model 5 -none- list
## repeats 1 -none- numeric
## bag 1 -none- logical
## names 99 -none- character
## terms 3 terms call
## coefnames 99 -none- character
## xlevels 0 -none- list
## xNames 99 -none- character
## problemType 1 -none- character
## tuneValue 3 data.frame list
## obsLevels 2 -none- character
## sold.fctr sold.fctr.predict.CSM.X.avNNet.N
## 1 N 522
## 2 Y 74
## sold.fctr.predict.CSM.X.avNNet.Y
## 1 26
## 2 396
## Prediction
## Reference N Y
## N 522 26
## Y 74 396
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.017682e-01 8.009293e-01 8.818153e-01 9.193597e-01 5.383104e-01
## AccuracyPValue McnemarPValue
## 1.594319e-140 2.601615e-06
## sold.fctr sold.fctr.predict.CSM.X.avNNet.N
## 1 N 371
## 2 Y 99
## sold.fctr.predict.CSM.X.avNNet.Y
## 1 76
## 2 284
## Prediction
## Reference N Y
## N 371 76
## Y 99 284
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.891566e-01 5.739595e-01 7.598002e-01 8.164427e-01 5.385542e-01
## AccuracyPValue McnemarPValue
## 3.415533e-51 9.630369e-02
## id
## 1 CSM.X.avNNet
## feats
## 1 spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 116.672 0.308
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8952788 0.9671533 0.8234043 0.9315849
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.8878924 0.8271026
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8818153 0.9193597 0.6493725
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.7797676 0.885906 0.6736292 0.8614202
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.3 0.7644684 0.7891566
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7598002 0.8164427 0.5739595
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01396042 0.02796881
## label step_major step_minor label_minor bgn end
## 10 fit.models_1_CSM.X 1 9 avNNet 634.001 755.463
## 11 fit.models_1_CSM.X 1 10 rf 755.464 NA
## elapsed
## 10 121.462
## 11 NA
## [1] "fitting model: CSM.X.rf"
## [1] " indep_vars: spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr"## Loading required package: randomForest
## randomForest 4.6-12
## Type rfNews() to see new features/changes/bug fixes.
##
## Attaching package: 'randomForest'
##
## The following object is masked from 'package:dplyr':
##
## combine
##
## The following object is masked from 'package:gdata':
##
## combine
##
## The following object is masked from 'package:ggplot2':
##
## margin
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 74 on full training set
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 1018 factor numeric
## err.rate 1500 -none- numeric
## confusion 6 -none- numeric
## votes 2036 matrix numeric
## oob.times 1018 -none- numeric
## classes 2 -none- character
## importance 99 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 1018 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 99 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## sold.fctr sold.fctr.predict.CSM.X.rf.N sold.fctr.predict.CSM.X.rf.Y
## 1 N 547 1
## 2 Y NA 470
## Prediction
## Reference N Y
## N 547 1
## Y 0 470
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.990177e-01 9.980241e-01 9.945391e-01 9.999751e-01 5.383104e-01
## AccuracyPValue McnemarPValue
## 1.358103e-271 1.000000e+00
## sold.fctr sold.fctr.predict.CSM.X.rf.N sold.fctr.predict.CSM.X.rf.Y
## 1 N 315 132
## 2 Y 67 316
## Prediction
## Reference N Y
## N 315 132
## Y 67 316
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.602410e-01 5.234042e-01 7.296990e-01 7.889219e-01 5.385542e-01
## AccuracyPValue McnemarPValue
## 6.593359e-40 5.710347e-06
## id
## 1 CSM.X.rf
## feats
## 1 spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 5 14.23 5.938
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.9990876 0.9981752 1 0.9999981
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.9989373 0.8310413
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.9945391 0.9999751 0.6583279
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.7831321 0.8456376 0.7206266 0.8492707
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.3 0.7605295 0.760241
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.729699 0.7889219 0.5234042
## label step_major step_minor label_minor bgn end
## 11 fit.models_1_CSM.X 1 10 rf 755.464 773.655
## 12 fit.models_1_CSM.X 1 11 earth 773.656 NA
## elapsed
## 11 18.191
## 12 NA
## [1] "fitting model: CSM.X.earth"
## [1] " indep_vars: spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting nprune = 12, degree = 1 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: degree
## Call: earth(x=matrix[1018,99], y=factor.object, keepxy=TRUE,
## glm=list(family=function.object), degree=1, nprune=12)
##
## GLM coefficients
## Y
## (Intercept) -2.210365
## spdiff.cut.fctr(-10,-1] 2.063762
## spdiff.cut.fctr(-1,0] 5.594756
## spdiff.cut.fctr(0,1] 5.793884
## spdiff.cut.fctr(1,10] 2.070603
## spdiff.cut.fctr(10,100] 1.489047
## spdiff.cut.fctr(-100,-10]:biddable 2.859710
## spdiff.cut.fctr(-10,-1]:biddable 2.387313
## spdiff.cut.fctr(1,10]:biddable 3.507058
## spdiff.cut.fctr(10,100]:biddable 4.397619
## spdiff.cut.fctr(100,1e+03]:biddable 4.289807
##
## Earth selected 11 of 54 terms, and 10 of 99 predictors
## Termination condition: RSq changed by less than 0.001 at 54 terms
## Importance: biddable-unused, spdiff.cut.fctr(10,100], ...
## Number of terms at each degree of interaction: 1 10 (additive model)
## Earth GCV 0.1203188 RSS 117.4833 GRSq 0.5168333 RSq 0.53565
##
## GLM null.deviance 1405.265 (1017 dof) deviance 758.8907 (1007 dof) iters 6
## sold.fctr sold.fctr.predict.CSM.X.earth.N
## 1 N 455
## 2 Y 77
## sold.fctr.predict.CSM.X.earth.Y
## 1 93
## 2 393
## Prediction
## Reference N Y
## N 455 93
## Y 77 393
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.330059e-01 6.648514e-01 8.086485e-01 8.554193e-01 5.383104e-01
## AccuracyPValue McnemarPValue
## 8.450416e-88 2.499596e-01
## sold.fctr sold.fctr.predict.CSM.X.earth.N
## 1 N 353
## 2 Y 73
## sold.fctr.predict.CSM.X.earth.Y
## 1 94
## 2 310
## Prediction
## Reference N Y
## N 353 94
## Y 73 310
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.987952e-01 5.967662e-01 7.698788e-01 8.255712e-01 5.385542e-01
## AccuracyPValue McnemarPValue
## 2.395646e-55 1.217074e-01
## id
## 1 CSM.X.earth
## feats
## 1 spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 5 8.246 0.937
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8328001 0.9124088 0.7531915 0.9006872
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.8221757 0.8241614
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8086485 0.8554193 0.6443967
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.8002815 0.8903803 0.7101828 0.8533245
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.4 0.7878018 0.7987952
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7698788 0.8255712 0.5967662
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.0121086 0.02385211
## label step_major step_minor label_minor bgn end
## 12 fit.models_1_CSM.X 1 11 earth 773.656 786.491
## 13 fit.models_1_RFE.X 1 12 setup 786.492 NA
## elapsed
## 12 12.835
## 13 NA## Warning in if (grepl("^%<d-%", indep_vars)) {: the condition has length > 1
## and only the first element will be used
## label step_major step_minor label_minor bgn end
## 13 fit.models_1_RFE.X 1 12 setup 786.492 786.503
## 14 fit.models_1_RFE.X 1 13 glmnet 786.504 NA
## elapsed
## 13 0.012
## 14 NA
## [1] "fitting model: RFE.X.glmnet"
## [1] " indep_vars: sprice.d20nexp,sprice.log10,sprice.root2,biddable,spdiff.cut.fctr,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.55, lambda = 0.00124 on full training set
## Length Class Mode
## a0 100 -none- numeric
## beta 9300 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 93 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## 1.63021978
## .rnorm
## -0.02801612
## D.chrs.pnct11.n.log
## 0.40561975
## D.chrs.pnct13.n.log
## -0.09288705
## D.ratio.weight.sum.wrds.n
## -0.08839642
## D.ratio.wrds.stop.n.wrds.n
## -0.25880885
## D.weight.post.stem.sum
## -0.04753825
## D.weight.sum
## -0.02322303
## D.weight.sum.stem.stop.Ratio
## -2.19018201
## biddable
## 2.41744637
## cellular.fctrUnknown
## -0.73950044
## color.fctrGold
## 0.02954856
## color.fctrSpace Gray
## -0.32846103
## color.fctrUnknown
## 0.32390652
## color.fctrWhite
## -0.20721212
## condition.fctrFor parts or not working
## -0.62794212
## condition.fctrManufacturer refurbished
## -1.07398396
## condition.fctrNew
## -0.01745976
## condition.fctrNew other (see details)
## 0.60902488
## condition.fctrSeller refurbished
## -0.96233940
## prdl.my.fctriPad1
## -0.62517912
## prdl.my.fctriPad2
## -0.42529882
## prdl.my.fctriPad3
## 0.10216597
## prdl.my.fctriPad4
## 1.07944310
## prdl.my.fctriPadAir
## 1.23565602
## prdl.my.fctriPadAir2
## 1.66029713
## prdl.my.fctriPadmini
## -0.46509412
## prdl.my.fctriPadmini2
## 1.09678817
## prdl.my.fctriPadmini3
## 0.55103428
## spdiff.cut.fctr(-100,-10]
## 2.04564296
## spdiff.cut.fctr(-10,-1]
## 3.77265013
## spdiff.cut.fctr(-1,0]
## 4.71456242
## spdiff.cut.fctr(0,1]
## 3.78173746
## spdiff.cut.fctr(1,10]
## 4.01508564
## spdiff.cut.fctr(10,100]
## 3.48091977
## spdiff.cut.fctr(100,1e+03]
## 0.76281336
## sprice.log10
## -0.24956516
## sprice.root2
## -0.12998047
## startprice.dcm1.is9
## -0.44624284
## startprice.dcm2.is9
## -0.08817862
## startprice.dgt2.is9
## 0.12405658
## storage.fctr16
## -0.09211417
## storage.fctr64
## 0.20763160
## storage.fctrUnknown
## 1.49205188
## cellular.fctr0:carrier.fctrNone
## 0.14909087
## cellular.fctr1:carrier.fctrSprint
## 0.93642895
## cellular.fctr1:carrier.fctrT-Mobile
## -0.27541726
## cellular.fctrUnknown:carrier.fctrUnknown
## -0.66554181
## cellular.fctr1:carrier.fctrVerizon
## 0.24316402
## prdl.my.fctrUnknown:.clusterid.fctr2
## 0.80275198
## prdl.my.fctriPad1:.clusterid.fctr2
## -0.52656480
## prdl.my.fctriPad3:.clusterid.fctr2
## 0.33067263
## prdl.my.fctriPad4:.clusterid.fctr2
## -1.08816529
## prdl.my.fctriPadAir:.clusterid.fctr2
## -0.17641114
## prdl.my.fctriPadmini2:.clusterid.fctr2
## -0.48059277
## prdl.my.fctrUnknown:.clusterid.fctr3
## -0.63242235
## prdl.my.fctriPad1:.clusterid.fctr3
## -0.87796569
## prdl.my.fctriPad4:.clusterid.fctr3
## -4.11287913
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## 1.70075206
## .rnorm
## -0.02949827
## D.chrs.pnct11.n.log
## 0.40977083
## D.chrs.pnct13.n.log
## -0.09943187
## D.ratio.weight.sum.wrds.n
## -0.08578622
## D.ratio.wrds.stop.n.wrds.n
## -0.32964948
## D.weight.post.stem.sum
## -0.05512101
## D.weight.sum
## -0.02628552
## D.weight.sum.stem.stop.Ratio
## -2.23281179
## biddable
## 2.43114198
## cellular.fctrUnknown
## -0.74699138
## color.fctrGold
## 0.03638316
## color.fctrSpace Gray
## -0.33206167
## color.fctrUnknown
## 0.32708190
## color.fctrWhite
## -0.21193393
## condition.fctrFor parts or not working
## -0.63292043
## condition.fctrManufacturer refurbished
## -1.08632863
## condition.fctrNew
## -0.01650105
## condition.fctrNew other (see details)
## 0.61790117
## condition.fctrSeller refurbished
## -0.97098587
## prdl.my.fctriPad1
## -0.62392816
## prdl.my.fctriPad2
## -0.42358424
## prdl.my.fctriPad3
## 0.11128137
## prdl.my.fctriPad4
## 1.11122219
## prdl.my.fctriPadAir
## 1.26425877
## prdl.my.fctriPadAir2
## 1.68539257
## prdl.my.fctriPadmini
## -0.46016598
## prdl.my.fctriPadmini2
## 1.12225971
## prdl.my.fctriPadmini3
## 0.57317549
## spdiff.cut.fctr(-100,-10]
## 2.07964768
## spdiff.cut.fctr(-10,-1]
## 3.81319148
## spdiff.cut.fctr(-1,0]
## 4.77674081
## spdiff.cut.fctr(0,1]
## 3.83636155
## spdiff.cut.fctr(1,10]
## 4.05767976
## spdiff.cut.fctr(10,100]
## 3.52093990
## spdiff.cut.fctr(100,1e+03]
## 0.79500598
## sprice.log10
## -0.24871435
## sprice.root2
## -0.13107580
## startprice.dcm1.is9
## -0.44793939
## startprice.dcm2.is9
## -0.09182489
## startprice.dgt2.is9
## 0.12708790
## storage.fctr16
## -0.09427495
## storage.fctr64
## 0.21013131
## storage.fctrUnknown
## 1.51798214
## cellular.fctr0:carrier.fctrNone
## 0.15271915
## cellular.fctr1:carrier.fctrSprint
## 0.94816261
## cellular.fctr1:carrier.fctrT-Mobile
## -0.27551939
## cellular.fctrUnknown:carrier.fctrUnknown
## -0.67320645
## cellular.fctr1:carrier.fctrVerizon
## 0.24770066
## prdl.my.fctrUnknown:.clusterid.fctr2
## 0.81184816
## prdl.my.fctriPad1:.clusterid.fctr2
## -0.54164033
## prdl.my.fctriPad3:.clusterid.fctr2
## 0.32647578
## prdl.my.fctriPad4:.clusterid.fctr2
## -1.12130109
## prdl.my.fctriPadAir:.clusterid.fctr2
## -0.18773554
## prdl.my.fctriPadmini2:.clusterid.fctr2
## -0.49147259
## prdl.my.fctrUnknown:.clusterid.fctr3
## -0.64952463
## prdl.my.fctriPad1:.clusterid.fctr3
## -0.89394903
## prdl.my.fctriPad4:.clusterid.fctr3
## -4.23223890
## sold.fctr sold.fctr.predict.RFE.X.glmnet.N
## 1 N 465
## 2 Y 63
## sold.fctr.predict.RFE.X.glmnet.Y
## 1 83
## 2 408
## Prediction
## Reference N Y
## N 465 83
## Y 63 408
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.567223e-01 7.126562e-01 8.336909e-01 8.776697e-01 5.377821e-01
## AccuracyPValue McnemarPValue
## 2.297444e-104 1.158460e-01
## sold.fctr sold.fctr.predict.RFE.X.glmnet.N
## 1 N 309
## 2 Y 59
## sold.fctr.predict.RFE.X.glmnet.Y
## 1 138
## 2 324
## Prediction
## Reference N Y
## N 309 138
## Y 59 324
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.626506e-01 5.294107e-01 7.322001e-01 7.912226e-01 5.385542e-01
## AccuracyPValue McnemarPValue
## 8.778953e-41 2.740269e-08
## id
## 1 RFE.X.glmnet
## feats
## 1 sprice.d20nexp,sprice.log10,sprice.root2,biddable,spdiff.cut.fctr,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 15.649 1.61
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8606455 0.899635 0.8216561 0.9339695
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.8482328 0.8259789
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8336909 0.8776697 0.6488269
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.7861198 0.8255034 0.7467363 0.8728278
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.3 0.7668639 0.7626506
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7322001 0.7912226 0.5294107
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01661812 0.03371297
## label step_major step_minor label_minor bgn end
## 14 fit.models_1_RFE.X 1 13 glmnet 786.504 806.635
## 15 fit.models_1_RFE.X 1 14 glm 806.635 NA
## elapsed
## 14 20.131
## 15 NA
## [1] "fitting model: RFE.X.glm"
## [1] " indep_vars: sprice.d20nexp,sprice.log10,sprice.root2,biddable,spdiff.cut.fctr,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr"
## + Fold1.Rep1: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold1.Rep1: parameter=none
## + Fold2.Rep1: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold2.Rep1: parameter=none
## + Fold3.Rep1: parameter=none## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold3.Rep1: parameter=none
## + Fold1.Rep2: parameter=none## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold1.Rep2: parameter=none
## + Fold2.Rep2: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold2.Rep2: parameter=none
## + Fold3.Rep2: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold3.Rep2: parameter=none
## + Fold1.Rep3: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold1.Rep3: parameter=none
## + Fold2.Rep3: parameter=none## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold2.Rep3: parameter=none
## + Fold3.Rep3: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold3.Rep3: parameter=none
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.8799 -0.4585 -0.0827 0.3157 3.4426
##
## Coefficients: (27 not defined because of singularities)
## Estimate Std. Error z value
## (Intercept) -24.013977 30.676258 -0.783
## .rnorm -0.017879 0.105728 -0.169
## D.chrs.n.log -6.596450 4.631508 -1.424
## D.chrs.pnct11.n.log 0.698920 0.420998 1.660
## D.chrs.pnct13.n.log 0.128271 0.415461 0.309
## D.chrs.uppr.n.log 4.346752 4.277265 1.016
## D.ratio.weight.sum.wrds.n 0.275885 0.327587 0.842
## D.ratio.wrds.stop.n.wrds.n -10.401584 4.110787 -2.530
## D.terms.post.stem.n.log 23.543358 11.243156 2.094
## D.terms.post.stop.n.log -30.277170 11.186964 -2.706
## D.weight.post.stem.sum -7.805252 5.537822 -1.409
## D.weight.post.stop.sum 7.281130 5.343106 1.363
## D.weight.sum NA NA NA
## D.weight.sum.stem.stop.Ratio 35.291129 31.015916 1.138
## D.wrds.n.log 7.888557 2.574678 3.064
## D.wrds.stop.n.log 0.283965 0.704658 0.403
## D.wrds.unq.n.log NA NA NA
## biddable 2.629761 0.296958 8.856
## cellular.fctr1 -0.208558 0.362309 -0.576
## cellular.fctrUnknown -1.825518 0.622783 -2.931
## color.fctrGold 0.093777 0.723749 0.130
## `color.fctrSpace Gray` -0.321920 0.423236 -0.761
## color.fctrUnknown 0.337091 0.296849 1.136
## color.fctrWhite -0.161321 0.330277 -0.488
## `condition.fctrFor parts or not working` -0.931556 0.457754 -2.035
## `condition.fctrManufacturer refurbished` -1.303710 0.841617 -1.549
## condition.fctrNew 0.051786 0.403722 0.128
## `condition.fctrNew other (see details)` 0.653459 0.567115 1.152
## `condition.fctrSeller refurbished` -1.091504 0.475361 -2.296
## prdl.my.fctriPad1 -0.663165 0.692858 -0.957
## prdl.my.fctriPad2 -0.432151 0.611092 -0.707
## prdl.my.fctriPad3 0.212661 0.643679 0.330
## prdl.my.fctriPad4 1.449155 0.714900 2.027
## prdl.my.fctriPadAir 1.645798 0.708082 2.324
## prdl.my.fctriPadAir2 1.802258 0.785987 2.293
## prdl.my.fctriPadmini -0.631408 0.598950 -1.054
## prdl.my.fctriPadmini2 1.176938 0.736815 1.597
## prdl.my.fctriPadmini3 0.564174 0.803880 0.702
## `spdiff.cut.fctr(-100,-10]` 2.478104 0.423101 5.857
## `spdiff.cut.fctr(-10,-1]` 4.361858 0.558559 7.809
## `spdiff.cut.fctr(-1,0]` 5.453459 1.021434 5.339
## `spdiff.cut.fctr(0,1]` 4.625198 1.318591 3.508
## `spdiff.cut.fctr(1,10]` 4.774135 0.563225 8.476
## `spdiff.cut.fctr(10,100]` 4.087659 0.506483 8.071
## `spdiff.cut.fctr(100,1e+03]` 1.217015 0.802479 1.517
## sprice.d20nexp -2.326349 3.454975 -0.673
## sprice.log10 -0.882586 1.088463 -0.811
## sprice.root2 -0.055361 0.160247 -0.345
## startprice.dcm1.is9 -0.512727 0.419493 -1.222
## startprice.dcm2.is9 -0.050518 0.408619 -0.124
## startprice.dgt1.is9 -0.016859 0.297735 -0.057
## startprice.dgt2.is9 0.062159 0.327567 0.190
## storage.fctr16 -0.008848 0.743448 -0.012
## storage.fctr32 0.153876 0.754968 0.204
## storage.fctr64 0.295482 0.737683 0.401
## storage.fctrUnknown 1.900231 0.954808 1.990
## `cellular.fctr0:carrier.fctrNone` NA NA NA
## `cellular.fctr1:carrier.fctrNone` NA NA NA
## `cellular.fctrUnknown:carrier.fctrNone` NA NA NA
## `cellular.fctr0:carrier.fctrOther` NA NA NA
## `cellular.fctr1:carrier.fctrOther` NA NA NA
## `cellular.fctrUnknown:carrier.fctrOther` NA NA NA
## `cellular.fctr0:carrier.fctrSprint` NA NA NA
## `cellular.fctr1:carrier.fctrSprint` 0.931237 0.782244 1.190
## `cellular.fctrUnknown:carrier.fctrSprint` NA NA NA
## `cellular.fctr0:carrier.fctrT-Mobile` NA NA NA
## `cellular.fctr1:carrier.fctrT-Mobile` 0.028737 1.282872 0.022
## `cellular.fctrUnknown:carrier.fctrT-Mobile` NA NA NA
## `cellular.fctr0:carrier.fctrUnknown` NA NA NA
## `cellular.fctr1:carrier.fctrUnknown` -0.006309 0.617559 -0.010
## `cellular.fctrUnknown:carrier.fctrUnknown` NA NA NA
## `cellular.fctr0:carrier.fctrVerizon` NA NA NA
## `cellular.fctr1:carrier.fctrVerizon` 0.416359 0.511901 0.813
## `cellular.fctrUnknown:carrier.fctrVerizon` NA NA NA
## `prdl.my.fctrUnknown:.clusterid.fctr2` 0.606245 0.883555 0.686
## `prdl.my.fctriPad1:.clusterid.fctr2` -1.343622 0.878667 -1.529
## `prdl.my.fctriPad2:.clusterid.fctr2` NA NA NA
## `prdl.my.fctriPad3:.clusterid.fctr2` 0.067398 0.975724 0.069
## `prdl.my.fctriPad4:.clusterid.fctr2` -1.953925 1.355454 -1.442
## `prdl.my.fctriPadAir:.clusterid.fctr2` -0.334021 0.949987 -0.352
## `prdl.my.fctriPadAir2:.clusterid.fctr2` NA NA NA
## `prdl.my.fctriPadmini:.clusterid.fctr2` NA NA NA
## `prdl.my.fctriPadmini2:.clusterid.fctr2` -0.692734 1.494218 -0.464
## `prdl.my.fctriPadmini3:.clusterid.fctr2` NA NA NA
## `prdl.my.fctrUnknown:.clusterid.fctr3` -0.076400 1.280283 -0.060
## `prdl.my.fctriPad1:.clusterid.fctr3` -1.353262 0.871409 -1.553
## `prdl.my.fctriPad2:.clusterid.fctr3` NA NA NA
## `prdl.my.fctriPad3:.clusterid.fctr3` NA NA NA
## `prdl.my.fctriPad4:.clusterid.fctr3` -16.522905 493.105582 -0.034
## `prdl.my.fctriPadAir:.clusterid.fctr3` NA NA NA
## `prdl.my.fctriPadAir2:.clusterid.fctr3` NA NA NA
## `prdl.my.fctriPadmini:.clusterid.fctr3` NA NA NA
## `prdl.my.fctriPadmini2:.clusterid.fctr3` NA NA NA
## `prdl.my.fctriPadmini3:.clusterid.fctr3` NA NA NA
## Pr(>|z|)
## (Intercept) 0.433733
## .rnorm 0.865713
## D.chrs.n.log 0.154373
## D.chrs.pnct11.n.log 0.096884 .
## D.chrs.pnct13.n.log 0.757516
## D.chrs.uppr.n.log 0.309512
## D.ratio.weight.sum.wrds.n 0.399691
## D.ratio.wrds.stop.n.wrds.n 0.011396 *
## D.terms.post.stem.n.log 0.036258 *
## D.terms.post.stop.n.log 0.006800 **
## D.weight.post.stem.sum 0.158704
## D.weight.post.stop.sum 0.172972
## D.weight.sum NA
## D.weight.sum.stem.stop.Ratio 0.255188
## D.wrds.n.log 0.002185 **
## D.wrds.stop.n.log 0.686961
## D.wrds.unq.n.log NA
## biddable < 2e-16 ***
## cellular.fctr1 0.564862
## cellular.fctrUnknown 0.003376 **
## color.fctrGold 0.896906
## `color.fctrSpace Gray` 0.446886
## color.fctrUnknown 0.256140
## color.fctrWhite 0.625236
## `condition.fctrFor parts or not working` 0.041845 *
## `condition.fctrManufacturer refurbished` 0.121369
## condition.fctrNew 0.897934
## `condition.fctrNew other (see details)` 0.249218
## `condition.fctrSeller refurbished` 0.021667 *
## prdl.my.fctriPad1 0.338494
## prdl.my.fctriPad2 0.479456
## prdl.my.fctriPad3 0.741110
## prdl.my.fctriPad4 0.042655 *
## prdl.my.fctriPadAir 0.020109 *
## prdl.my.fctriPadAir2 0.021849 *
## prdl.my.fctriPadmini 0.291796
## prdl.my.fctriPadmini2 0.110192
## prdl.my.fctriPadmini3 0.482795
## `spdiff.cut.fctr(-100,-10]` 4.71e-09 ***
## `spdiff.cut.fctr(-10,-1]` 5.76e-15 ***
## `spdiff.cut.fctr(-1,0]` 9.34e-08 ***
## `spdiff.cut.fctr(0,1]` 0.000452 ***
## `spdiff.cut.fctr(1,10]` < 2e-16 ***
## `spdiff.cut.fctr(10,100]` 6.99e-16 ***
## `spdiff.cut.fctr(100,1e+03]` 0.129375
## sprice.d20nexp 0.500735
## sprice.log10 0.417449
## sprice.root2 0.729739
## startprice.dcm1.is9 0.221611
## startprice.dcm2.is9 0.901608
## startprice.dgt1.is9 0.954845
## startprice.dgt2.is9 0.849498
## storage.fctr16 0.990505
## storage.fctr32 0.838496
## storage.fctr64 0.688748
## storage.fctrUnknown 0.046572 *
## `cellular.fctr0:carrier.fctrNone` NA
## `cellular.fctr1:carrier.fctrNone` NA
## `cellular.fctrUnknown:carrier.fctrNone` NA
## `cellular.fctr0:carrier.fctrOther` NA
## `cellular.fctr1:carrier.fctrOther` NA
## `cellular.fctrUnknown:carrier.fctrOther` NA
## `cellular.fctr0:carrier.fctrSprint` NA
## `cellular.fctr1:carrier.fctrSprint` 0.233863
## `cellular.fctrUnknown:carrier.fctrSprint` NA
## `cellular.fctr0:carrier.fctrT-Mobile` NA
## `cellular.fctr1:carrier.fctrT-Mobile` 0.982129
## `cellular.fctrUnknown:carrier.fctrT-Mobile` NA
## `cellular.fctr0:carrier.fctrUnknown` NA
## `cellular.fctr1:carrier.fctrUnknown` 0.991849
## `cellular.fctrUnknown:carrier.fctrUnknown` NA
## `cellular.fctr0:carrier.fctrVerizon` NA
## `cellular.fctr1:carrier.fctrVerizon` 0.416013
## `cellular.fctrUnknown:carrier.fctrVerizon` NA
## `prdl.my.fctrUnknown:.clusterid.fctr2` 0.492623
## `prdl.my.fctriPad1:.clusterid.fctr2` 0.126225
## `prdl.my.fctriPad2:.clusterid.fctr2` NA
## `prdl.my.fctriPad3:.clusterid.fctr2` 0.944930
## `prdl.my.fctriPad4:.clusterid.fctr2` 0.149436
## `prdl.my.fctriPadAir:.clusterid.fctr2` 0.725134
## `prdl.my.fctriPadAir2:.clusterid.fctr2` NA
## `prdl.my.fctriPadmini:.clusterid.fctr2` NA
## `prdl.my.fctriPadmini2:.clusterid.fctr2` 0.642927
## `prdl.my.fctriPadmini3:.clusterid.fctr2` NA
## `prdl.my.fctrUnknown:.clusterid.fctr3` 0.952415
## `prdl.my.fctriPad1:.clusterid.fctr3` 0.120433
## `prdl.my.fctriPad2:.clusterid.fctr3` NA
## `prdl.my.fctriPad3:.clusterid.fctr3` NA
## `prdl.my.fctriPad4:.clusterid.fctr3` 0.973270
## `prdl.my.fctriPadAir:.clusterid.fctr3` NA
## `prdl.my.fctriPadAir2:.clusterid.fctr3` NA
## `prdl.my.fctriPadmini:.clusterid.fctr3` NA
## `prdl.my.fctriPadmini2:.clusterid.fctr3` NA
## `prdl.my.fctriPadmini3:.clusterid.fctr3` NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1406.81 on 1018 degrees of freedom
## Residual deviance: 633.39 on 952 degrees of freedom
## AIC: 767.39
##
## Number of Fisher Scoring iterations: 15## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## sold.fctr sold.fctr.predict.RFE.X.glm.N sold.fctr.predict.RFE.X.glm.Y
## 1 N 469 79
## 2 Y 65 406
## Prediction
## Reference N Y
## N 469 79
## Y 65 406
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.586850e-01 7.163393e-01 8.357707e-01 8.795035e-01 5.377821e-01
## AccuracyPValue McnemarPValue
## 8.576365e-106 2.786605e-01## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## sold.fctr sold.fctr.predict.RFE.X.glm.N sold.fctr.predict.RFE.X.glm.Y
## 1 N 367 80
## 2 Y 96 287
## Prediction
## Reference N Y
## N 367 80
## Y 96 287
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.879518e-01 5.720872e-01 7.585420e-01 8.152999e-01 5.385542e-01
## AccuracyPValue McnemarPValue
## 1.092681e-50 2.581950e-01
## id
## 1 RFE.X.glm
## feats
## 1 sprice.d20nexp,sprice.log10,sprice.root2,biddable,spdiff.cut.fctr,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 2.905 0.19
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.859584 0.899635 0.8195329 0.9391534
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.8493724 0.8217402
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8357707 0.8795035 0.6404864
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.7851882 0.8210291 0.7493473 0.8700066
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.7653333 0.7879518
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.758542 0.8152999 0.5720872
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.02794287 0.05637771
## label step_major step_minor label_minor bgn end
## 15 fit.models_1_RFE.X 1 14 glm 806.635 813.969
## 16 fit.models_1_All.X 1 15 setup 813.970 NA
## elapsed
## 15 7.334
## 16 NA## Warning in if (grepl("^%<d-%", indep_vars)) {: the condition has length > 1
## and only the first element will be used
## label step_major step_minor label_minor bgn end
## 16 fit.models_1_All.X 1 15 setup 813.970 813.977
## 17 fit.models_1_All.X 1 16 glmnet 813.978 NA
## elapsed
## 16 0.007
## 17 NA
## [1] "fitting model: All.X.glmnet"
## [1] " indep_vars: biddable,sprice.d20nexp,spdiff.cut.fctr,D.ratio.wrds.stop.n.wrds.n,startprice.dcm2.is9,D.wrds.stop.n.log,D.weight.sum.stem.stop.Ratio,D.ratio.weight.sum.wrds.n,.rnorm,startprice.dcm1.is9,storage.fctr,D.chrs.pnct11.n.log,D.chrs.pnct13.n.log,startprice.dgt2.is9,color.fctr,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,prdl.my.fctr,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.n.log,D.terms.post.stem.n.log,D.wrds.unq.n.log,D.terms.post.stop.n.log,cellular.fctr,startprice.dgt1.is9,condition.fctr,sprice.log10,sprice.root2,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.000267 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda
## Length Class Mode
## a0 100 -none- numeric
## beta 9300 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 93 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## 5.9298599950
## .rnorm
## -0.0367487387
## D.chrs.n.log
## -0.2852958123
## D.chrs.pnct11.n.log
## 0.4714450805
## D.chrs.pnct13.n.log
## -0.1653784579
## D.chrs.uppr.n.log
## -0.0116026565
## D.ratio.weight.sum.wrds.n
## -0.0593199934
## D.ratio.wrds.stop.n.wrds.n
## -2.9071388332
## D.terms.post.stem.n.log
## -0.2864766681
## D.terms.post.stop.n.log
## -0.9265313808
## D.weight.post.stem.sum
## -0.1209650771
## D.weight.sum
## -0.1180951219
## D.weight.sum.stem.stop.Ratio
## -2.9013605803
## D.wrds.n.log
## 1.2607015439
## D.wrds.stop.n.log
## 0.1152103538
## D.wrds.unq.n.log
## -0.1770678274
## biddable
## 2.5343114368
## cellular.fctrUnknown
## -0.7922522457
## color.fctrGold
## 0.0871372337
## color.fctrSpace Gray
## -0.3474018561
## color.fctrUnknown
## 0.3560862355
## color.fctrWhite
## -0.2253657549
## condition.fctrFor parts or not working
## -0.7365978083
## condition.fctrManufacturer refurbished
## -1.1969859100
## condition.fctrNew
## 0.0297417566
## condition.fctrNew other (see details)
## 0.7229925582
## condition.fctrSeller refurbished
## -1.0271638293
## prdl.my.fctriPad1
## -0.6856562063
## prdl.my.fctriPad2
## -0.4622200902
## prdl.my.fctriPad3
## 0.1837795731
## prdl.my.fctriPad4
## 1.4053112779
## prdl.my.fctriPadAir
## 1.5486082406
## prdl.my.fctriPadAir2
## 1.9064724435
## prdl.my.fctriPadmini
## -0.4755225796
## prdl.my.fctriPadmini2
## 1.3324979842
## prdl.my.fctriPadmini3
## 0.7573021470
## spdiff.cut.fctr(-100,-10]
## 2.3577551693
## spdiff.cut.fctr(-10,-1]
## 4.1547129846
## spdiff.cut.fctr(-1,0]
## 5.3348860714
## spdiff.cut.fctr(0,1]
## 4.4072812301
## spdiff.cut.fctr(1,10]
## 4.4073453219
## spdiff.cut.fctr(10,100]
## 3.8533894456
## spdiff.cut.fctr(100,1e+03]
## 1.0918155713
## sprice.d20nexp
## -1.5657841541
## sprice.log10
## -0.5769819282
## sprice.root2
## -0.1085201562
## startprice.dcm1.is9
## -0.4894970169
## startprice.dcm2.is9
## -0.0838883551
## startprice.dgt1.is9
## 0.0254181773
## startprice.dgt2.is9
## 0.1157259996
## storage.fctr16
## -0.1300313626
## storage.fctr32
## 0.0005589105
## storage.fctr64
## 0.2180851783
## storage.fctrUnknown
## 1.7336441556
## cellular.fctr0:carrier.fctrNone
## 0.1706810167
## cellular.fctr1:carrier.fctrOther
## 2.6072585524
## cellular.fctr1:carrier.fctrSprint
## 1.0270604082
## cellular.fctr1:carrier.fctrT-Mobile
## -0.2821763037
## cellular.fctr1:carrier.fctrUnknown
## -0.0179172973
## cellular.fctrUnknown:carrier.fctrUnknown
## -0.7883348835
## cellular.fctr1:carrier.fctrVerizon
## 0.3156224380
## prdl.my.fctrUnknown:.clusterid.fctr2
## 0.8932344300
## prdl.my.fctriPad1:.clusterid.fctr2
## -0.7404516345
## prdl.my.fctriPad3:.clusterid.fctr2
## 0.2701330403
## prdl.my.fctriPad4:.clusterid.fctr2
## -1.4552193885
## prdl.my.fctriPadAir:.clusterid.fctr2
## -0.3279730199
## prdl.my.fctriPadmini2:.clusterid.fctr2
## -0.6287767988
## prdl.my.fctrUnknown:.clusterid.fctr3
## -0.7598398151
## prdl.my.fctriPad1:.clusterid.fctr3
## -1.1337789530
## prdl.my.fctriPad4:.clusterid.fctr3
## -5.9466674332
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)"
## [2] ".rnorm"
## [3] "D.chrs.n.log"
## [4] "D.chrs.pnct11.n.log"
## [5] "D.chrs.pnct13.n.log"
## [6] "D.chrs.uppr.n.log"
## [7] "D.ratio.weight.sum.wrds.n"
## [8] "D.ratio.wrds.stop.n.wrds.n"
## [9] "D.terms.post.stem.n.log"
## [10] "D.terms.post.stop.n.log"
## [11] "D.weight.post.stem.sum"
## [12] "D.weight.post.stop.sum"
## [13] "D.weight.sum"
## [14] "D.weight.sum.stem.stop.Ratio"
## [15] "D.wrds.n.log"
## [16] "D.wrds.stop.n.log"
## [17] "D.wrds.unq.n.log"
## [18] "biddable"
## [19] "cellular.fctr1"
## [20] "cellular.fctrUnknown"
## [21] "color.fctrGold"
## [22] "color.fctrSpace Gray"
## [23] "color.fctrUnknown"
## [24] "color.fctrWhite"
## [25] "condition.fctrFor parts or not working"
## [26] "condition.fctrManufacturer refurbished"
## [27] "condition.fctrNew"
## [28] "condition.fctrNew other (see details)"
## [29] "condition.fctrSeller refurbished"
## [30] "prdl.my.fctriPad1"
## [31] "prdl.my.fctriPad2"
## [32] "prdl.my.fctriPad3"
## [33] "prdl.my.fctriPad4"
## [34] "prdl.my.fctriPadAir"
## [35] "prdl.my.fctriPadAir2"
## [36] "prdl.my.fctriPadmini"
## [37] "prdl.my.fctriPadmini2"
## [38] "prdl.my.fctriPadmini3"
## [39] "spdiff.cut.fctr(-100,-10]"
## [40] "spdiff.cut.fctr(-10,-1]"
## [41] "spdiff.cut.fctr(-1,0]"
## [42] "spdiff.cut.fctr(0,1]"
## [43] "spdiff.cut.fctr(1,10]"
## [44] "spdiff.cut.fctr(10,100]"
## [45] "spdiff.cut.fctr(100,1e+03]"
## [46] "sprice.d20nexp"
## [47] "sprice.log10"
## [48] "sprice.root2"
## [49] "startprice.dcm1.is9"
## [50] "startprice.dcm2.is9"
## [51] "startprice.dgt1.is9"
## [52] "startprice.dgt2.is9"
## [53] "storage.fctr16"
## [54] "storage.fctr32"
## [55] "storage.fctr64"
## [56] "storage.fctrUnknown"
## [57] "cellular.fctr0:carrier.fctrNone"
## [58] "cellular.fctr1:carrier.fctrNone"
## [59] "cellular.fctrUnknown:carrier.fctrNone"
## [60] "cellular.fctr0:carrier.fctrOther"
## [61] "cellular.fctr1:carrier.fctrOther"
## [62] "cellular.fctrUnknown:carrier.fctrOther"
## [63] "cellular.fctr0:carrier.fctrSprint"
## [64] "cellular.fctr1:carrier.fctrSprint"
## [65] "cellular.fctrUnknown:carrier.fctrSprint"
## [66] "cellular.fctr0:carrier.fctrT-Mobile"
## [67] "cellular.fctr1:carrier.fctrT-Mobile"
## [68] "cellular.fctrUnknown:carrier.fctrT-Mobile"
## [69] "cellular.fctr0:carrier.fctrUnknown"
## [70] "cellular.fctr1:carrier.fctrUnknown"
## [71] "cellular.fctrUnknown:carrier.fctrUnknown"
## [72] "cellular.fctr0:carrier.fctrVerizon"
## [73] "cellular.fctr1:carrier.fctrVerizon"
## [74] "cellular.fctrUnknown:carrier.fctrVerizon"
## [75] "prdl.my.fctrUnknown:.clusterid.fctr2"
## [76] "prdl.my.fctriPad1:.clusterid.fctr2"
## [77] "prdl.my.fctriPad2:.clusterid.fctr2"
## [78] "prdl.my.fctriPad3:.clusterid.fctr2"
## [79] "prdl.my.fctriPad4:.clusterid.fctr2"
## [80] "prdl.my.fctriPadAir:.clusterid.fctr2"
## [81] "prdl.my.fctriPadAir2:.clusterid.fctr2"
## [82] "prdl.my.fctriPadmini:.clusterid.fctr2"
## [83] "prdl.my.fctriPadmini2:.clusterid.fctr2"
## [84] "prdl.my.fctriPadmini3:.clusterid.fctr2"
## [85] "prdl.my.fctrUnknown:.clusterid.fctr3"
## [86] "prdl.my.fctriPad1:.clusterid.fctr3"
## [87] "prdl.my.fctriPad2:.clusterid.fctr3"
## [88] "prdl.my.fctriPad3:.clusterid.fctr3"
## [89] "prdl.my.fctriPad4:.clusterid.fctr3"
## [90] "prdl.my.fctriPadAir:.clusterid.fctr3"
## [91] "prdl.my.fctriPadAir2:.clusterid.fctr3"
## [92] "prdl.my.fctriPadmini:.clusterid.fctr3"
## [93] "prdl.my.fctriPadmini2:.clusterid.fctr3"
## [94] "prdl.my.fctriPadmini3:.clusterid.fctr3"
## sold.fctr sold.fctr.predict.All.X.glmnet.N
## 1 N 467
## 2 Y 63
## sold.fctr.predict.All.X.glmnet.Y
## 1 81
## 2 409
## Prediction
## Reference N Y
## N 467 81
## Y 63 409
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.588235e-01 7.168196e-01 8.359295e-01 8.796230e-01 5.372549e-01
## AccuracyPValue McnemarPValue
## 2.677951e-106 1.565804e-01
## sold.fctr sold.fctr.predict.All.X.glmnet.N
## 1 N 309
## 2 Y 60
## sold.fctr.predict.All.X.glmnet.Y
## 1 138
## 2 323
## Prediction
## Reference N Y
## N 309 138
## Y 60 323
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.614458e-01 5.269348e-01 7.309494e-01 7.900724e-01 5.385542e-01
## AccuracyPValue McnemarPValue
## 2.413828e-40 4.446039e-08
## id
## 1 All.X.glmnet
## feats
## 1 biddable,sprice.d20nexp,spdiff.cut.fctr,D.ratio.wrds.stop.n.wrds.n,startprice.dcm2.is9,D.wrds.stop.n.log,D.weight.sum.stem.stop.Ratio,D.ratio.weight.sum.wrds.n,.rnorm,startprice.dcm1.is9,storage.fctr,D.chrs.pnct11.n.log,D.chrs.pnct13.n.log,startprice.dgt2.is9,color.fctr,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,prdl.my.fctr,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.n.log,D.terms.post.stem.n.log,D.wrds.unq.n.log,D.terms.post.stop.n.log,cellular.fctr,startprice.dgt1.is9,condition.fctr,sprice.log10,sprice.root2,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 14.564 0.515
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8626593 0.9032847 0.8220339 0.9355785
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.8503119 0.8303886
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8359295 0.879623 0.6576319
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.7835089 0.8255034 0.7415144 0.8721444
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.3 0.7654028 0.7614458
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7309494 0.7900724 0.5269348
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01126649 0.02276618
## label step_major step_minor label_minor bgn
## 17 fit.models_1_All.X 1 16 glmnet 813.978
## 18 fit.models_1_Best.Interact 1 17 setup 833.005
## end elapsed
## 17 833.004 19.026
## 18 NA NA## Warning in if (grepl("^%<d-%", indep_vars)) {: the condition has length > 1
## and only the first element will be used
## label step_major step_minor label_minor bgn
## 18 fit.models_1_Best.Interact 1 17 setup 833.005
## 19 fit.models_1_CSM.X.Interact 1 18 glmnet 833.038
## end elapsed
## 18 833.038 0.033
## 19 NA NA
## [1] "fitting model: CSM.X.Interact.glmnet"
## [1] " indep_vars: .rnorm,D.weight.sum,D.weight.post.stem.sum,D.chrs.pnct13.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,startprice.dcm2.is9,sprice.root2,sprice.log10,startprice.dcm1.is9,D.weight.sum.stem.stop.Ratio,spdiff.cut.fctr*biddable,spdiff.cut.fctr*storage.fctr,spdiff.cut.fctr*prdl.my.fctr,spdiff.cut.fctr*cellular.fctr,spdiff.cut.fctr*D.chrs.pnct11.n.log,spdiff.cut.fctr*condition.fctr,spdiff.cut.fctr*color.fctr,spdiff.cut.fctr*sprice.d20nexp,spdiff.cut.fctr*startprice.dgt2.is9,spdiff.cut.fctr*D.wrds.n.log,spdiff.cut.fctr*D.chrs.n.log,spdiff.cut.fctr*D.chrs.uppr.n.log,spdiff.cut.fctr*D.terms.post.stem.n.log,spdiff.cut.fctr*D.terms.post.stop.n.log,spdiff.cut.fctr*D.wrds.stop.n.log,spdiff.cut.fctr*D.wrds.unq.n.log,spdiff.cut.fctr*startprice.dgt1.is9,spdiff.cut.fctr*D.weight.post.stop.sum,spdiff.cut.fctr*cellular.fctr:carrier.fctr,spdiff.cut.fctr*prdl.my.fctr:.clusterid.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.124 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda
## Length Class Mode
## a0 100 -none- numeric
## beta 61800 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 618 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## 0.2003544217
## D.ratio.wrds.stop.n.wrds.n
## 0.0092867024
## D.weight.post.stem.sum
## -0.0038389521
## D.weight.sum
## -0.0038913503
## spdiff.cut.fctr(-10,-1]
## 0.3476738359
## spdiff.cut.fctr(-1,0]
## 0.4028101374
## spdiff.cut.fctr(0,1]
## 0.3335824881
## spdiff.cut.fctr(1,10]
## 0.3991130156
## spdiff.cut.fctr(10,100]
## 0.2236232117
## spdiff.cut.fctr(100,1e+03]
## -0.1334593837
## D.terms.post.stem.n.log
## -0.0032541556
## D.terms.post.stop.n.log
## -0.0046135795
## D.weight.post.stop.sum
## -0.0004194837
## D.wrds.unq.n.log
## -0.0033282182
## biddable
## 0.7973311871
## condition.fctrManufacturer refurbished
## -0.0277305643
## condition.fctrNew
## -0.0498113388
## condition.fctrSeller refurbished
## -0.1040395431
## prdl.my.fctriPad3
## 0.0337454120
## sprice.d20nexp
## 0.4178641721
## startprice.dgt1.is9
## -0.0570821468
## sprice.log10
## -0.1208220761
## sprice.root2
## -0.0479464672
## startprice.dcm1.is9
## -0.0575668655
## spdiff.cut.fctr(-1,0]:D.chrs.n.log
## 0.0103703973
## spdiff.cut.fctr(-10,-1]:D.chrs.pnct11.n.log
## 0.2532195585
## spdiff.cut.fctr(1,10]:D.chrs.pnct11.n.log
## 0.1140225926
## spdiff.cut.fctr(10,100]:D.chrs.pnct11.n.log
## 0.0009323088
## spdiff.cut.fctr(-1,0]:D.chrs.uppr.n.log
## 0.0108335319
## spdiff.cut.fctr(-1,0]:D.terms.post.stem.n.log
## 0.0181363307
## spdiff.cut.fctr(-1,0]:D.terms.post.stop.n.log
## 0.0179588166
## spdiff.cut.fctr(-1,0]:D.weight.post.stop.sum
## 0.0156246613
## spdiff.cut.fctr(-1,0]:D.wrds.n.log
## 0.0149762990
## spdiff.cut.fctr(-1,0]:D.wrds.unq.n.log
## 0.0183656990
## spdiff.cut.fctr(-100,-10]:biddable
## 0.5205095418
## spdiff.cut.fctr(-10,-1]:biddable
## 0.4560413971
## spdiff.cut.fctr(-1,0]:biddable
## 0.2859921111
## spdiff.cut.fctr(0,1]:biddable
## 0.2543775159
## spdiff.cut.fctr(1,10]:biddable
## 0.6122637313
## spdiff.cut.fctr(10,100]:biddable
## 0.8476797429
## spdiff.cut.fctr(100,1e+03]:biddable
## 0.5095000083
## spdiff.cut.fctr(-1,0]:cellular.fctr1
## 0.0189719978
## spdiff.cut.fctr(0,1]:cellular.fctr1
## 0.2039117187
## spdiff.cut.fctr(1,10]:cellular.fctr1
## 0.1130280451
## spdiff.cut.fctr(10,100]:cellular.fctr1
## 0.0857190132
## spdiff.cut.fctr(-100,-10]:cellular.fctrUnknown
## -0.0856449143
## spdiff.cut.fctr(-10,-1]:cellular.fctrUnknown
## 0.1654447531
## spdiff.cut.fctr(100,1e+03]:cellular.fctrUnknown
## -0.1166325605
## spdiff.cut.fctr(-100,-10]:color.fctrGold
## 0.0360822576
## spdiff.cut.fctr(1,10]:color.fctrGold
## 0.2816433539
## spdiff.cut.fctr(-10,-1]:color.fctrUnknown
## 0.3623011098
## spdiff.cut.fctr(-1,0]:color.fctrUnknown
## 0.0023109339
## spdiff.cut.fctr(10,100]:color.fctrUnknown
## 0.2566272345
## spdiff.cut.fctr(-100,-10]:condition.fctrNew other (see details)
## -0.0834404434
## spdiff.cut.fctr(1,10]:condition.fctrNew other (see details)
## 0.9058550869
## spdiff.cut.fctr(10,100]:condition.fctrNew other (see details)
## 0.6358847615
## spdiff.cut.fctr(100,1e+03]:condition.fctrNew other (see details)
## 0.8486980836
## spdiff.cut.fctr(-100,-10]:condition.fctrSeller refurbished
## -0.1679008442
## spdiff.cut.fctr(-10,-1]:condition.fctrSeller refurbished
## -0.3575990091
## spdiff.cut.fctr(-100,-10]:prdl.my.fctriPad1
## -0.1899625991
## spdiff.cut.fctr(-10,-1]:prdl.my.fctriPad1
## 0.0650113482
## spdiff.cut.fctr(0,1]:prdl.my.fctriPad1
## 0.3136238146
## spdiff.cut.fctr(10,100]:prdl.my.fctriPad1
## 0.2942888705
## spdiff.cut.fctr(1,10]:prdl.my.fctriPad2
## 0.1463508627
## spdiff.cut.fctr(10,100]:prdl.my.fctriPad2
## -0.1636926158
## spdiff.cut.fctr(-1,0]:prdl.my.fctriPad3
## 0.0952932059
## spdiff.cut.fctr(1,10]:prdl.my.fctriPad3
## 0.0096086251
## spdiff.cut.fctr(100,1e+03]:prdl.my.fctriPad3
## -0.4362949863
## spdiff.cut.fctr(10,100]:prdl.my.fctriPad4
## 0.6132097901
## spdiff.cut.fctr(-10,-1]:prdl.my.fctriPadAir
## 0.9042060015
## spdiff.cut.fctr(10,100]:prdl.my.fctriPadAir
## 0.8888627557
## spdiff.cut.fctr(-100,-10]:prdl.my.fctriPadAir2
## 0.0816721294
## spdiff.cut.fctr(10,100]:prdl.my.fctriPadAir2
## 0.4328720059
## spdiff.cut.fctr(100,1e+03]:prdl.my.fctriPadAir2
## 0.5620197169
## spdiff.cut.fctr(-100,-10]:prdl.my.fctriPadmini
## -0.0300101775
## spdiff.cut.fctr(-10,-1]:prdl.my.fctriPadmini
## 0.0640500302
## spdiff.cut.fctr(1,10]:prdl.my.fctriPadmini
## 0.1896191710
## spdiff.cut.fctr(10,100]:prdl.my.fctriPadmini
## -0.0461816988
## spdiff.cut.fctr(-10,-1]:prdl.my.fctriPadmini2
## 0.4023989009
## spdiff.cut.fctr(10,100]:prdl.my.fctriPadmini2
## 0.7081966709
## prdl.my.fctriPad4:.clusterid.fctr3
## -0.3341478694
## spdiff.cut.fctr(-100,-10]:sprice.d20nexp
## 2.5892324297
## spdiff.cut.fctr(-1,0]:sprice.d20nexp
## 0.1636370163
## spdiff.cut.fctr(0,1]:sprice.d20nexp
## 0.0443816058
## spdiff.cut.fctr(1,10]:sprice.d20nexp
## 0.0549666682
## spdiff.cut.fctr(100,1e+03]:sprice.d20nexp
## -0.0826004172
## spdiff.cut.fctr(-10,-1]:startprice.dgt1.is9
## 0.0275028792
## spdiff.cut.fctr(0,1]:startprice.dgt1.is9
## 0.0387454109
## spdiff.cut.fctr(100,1e+03]:startprice.dgt1.is9
## -0.3027875717
## spdiff.cut.fctr(-1,0]:startprice.dgt2.is9
## 0.2770374419
## spdiff.cut.fctr(0,1]:startprice.dgt2.is9
## 0.3232065866
## spdiff.cut.fctr(10,100]:startprice.dgt2.is9
## 0.0918421360
## spdiff.cut.fctr(-100,-10]:storage.fctr16
## -0.1436018666
## spdiff.cut.fctr(-10,-1]:storage.fctr16
## 0.0891435766
## spdiff.cut.fctr(-1,0]:storage.fctr16
## 0.0992412003
## spdiff.cut.fctr(0,1]:storage.fctr16
## 0.1917331476
## spdiff.cut.fctr(1,10]:storage.fctr16
## 0.2868299652
## spdiff.cut.fctr(10,100]:storage.fctr16
## 0.1306332527
## spdiff.cut.fctr(100,1e+03]:storage.fctr16
## -0.2861678033
## spdiff.cut.fctr(-10,-1]:storage.fctr32
## 0.1386303573
## spdiff.cut.fctr(-100,-10]:storage.fctr64
## 0.0481397591
## spdiff.cut.fctr(-1,0]:storage.fctr64
## 0.3273254729
## spdiff.cut.fctr(10,100]:storage.fctr64
## 0.1217352232
## spdiff.cut.fctr(-10,-1]:storage.fctrUnknown
## 0.1645376731
## spdiff.cut.fctr(100,1e+03]:storage.fctrUnknown
## -0.1385361653
## spdiff.cut.fctr(-10,-1]:cellular.fctr0:carrier.fctrNone
## 0.2181376190
## spdiff.cut.fctr(-1,0]:cellular.fctr0:carrier.fctrNone
## 0.2502845214
## spdiff.cut.fctr(1,10]:cellular.fctr0:carrier.fctrNone
## 0.2339119465
## spdiff.cut.fctr(10,100]:cellular.fctr0:carrier.fctrNone
## 0.0877674067
## spdiff.cut.fctr(-100,-10]:cellular.fctr1:carrier.fctrSprint
## 0.2293886176
## spdiff.cut.fctr(-10,-1]:cellular.fctr1:carrier.fctrT-Mobile
## 0.3763377869
## spdiff.cut.fctr(-100,-10]:cellular.fctr1:carrier.fctrUnknown
## -0.1592810564
## spdiff.cut.fctr(1,10]:cellular.fctr1:carrier.fctrUnknown
## 0.4062502805
## spdiff.cut.fctr(100,1e+03]:cellular.fctr1:carrier.fctrUnknown
## 0.3857991307
## spdiff.cut.fctr(-100,-10]:cellular.fctrUnknown:carrier.fctrUnknown
## -0.0853931291
## spdiff.cut.fctr(-10,-1]:cellular.fctrUnknown:carrier.fctrUnknown
## 0.1634169145
## spdiff.cut.fctr(100,1e+03]:cellular.fctrUnknown:carrier.fctrUnknown
## -0.1160636633
## spdiff.cut.fctr(-10,-1]:prdl.my.fctriPad1:.clusterid.fctr2
## 0.2859264548
## spdiff.cut.fctr(1,10]:prdl.my.fctriPad1:.clusterid.fctr2
## -0.2182896101
## spdiff.cut.fctr(-1,0]:prdl.my.fctriPad3:.clusterid.fctr2
## 0.2759346233
## spdiff.cut.fctr(1,10]:prdl.my.fctriPadmini2:.clusterid.fctr2
## -0.9504442378
## spdiff.cut.fctr(10,100]:prdl.my.fctriPadmini2:.clusterid.fctr2
## 0.0374612770
## spdiff.cut.fctr(-100,-10]:prdl.my.fctrUnknown:.clusterid.fctr3
## -0.0981376553
## spdiff.cut.fctr(1,10]:prdl.my.fctrUnknown:.clusterid.fctr3
## 0.9653155897
## spdiff.cut.fctr(100,1e+03]:prdl.my.fctrUnknown:.clusterid.fctr3
## -0.0576722654
## spdiff.cut.fctr(1,10]:prdl.my.fctriPad1:.clusterid.fctr3
## -0.3497943623
## spdiff.cut.fctr(10,100]:prdl.my.fctriPad1:.clusterid.fctr3
## 0.0416636823
## spdiff.cut.fctr(-100,-10]:prdl.my.fctriPad4:.clusterid.fctr3
## -0.0146569722
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## 2.046418e-01
## D.ratio.wrds.stop.n.wrds.n
## 9.738408e-03
## D.weight.post.stem.sum
## -4.501123e-03
## D.weight.sum
## -4.537000e-03
## spdiff.cut.fctr(-10,-1]
## 3.628271e-01
## spdiff.cut.fctr(-1,0]
## 4.197685e-01
## spdiff.cut.fctr(0,1]
## 3.464818e-01
## spdiff.cut.fctr(1,10]
## 4.130895e-01
## spdiff.cut.fctr(10,100]
## 2.345640e-01
## spdiff.cut.fctr(100,1e+03]
## -1.434870e-01
## D.terms.post.stem.n.log
## -3.292097e-03
## D.terms.post.stop.n.log
## -4.861456e-03
## D.weight.post.stop.sum
## -7.818681e-04
## D.wrds.unq.n.log
## -3.192700e-03
## biddable
## 8.115305e-01
## condition.fctrManufacturer refurbished
## -6.123392e-02
## condition.fctrNew
## -5.295053e-02
## condition.fctrSeller refurbished
## -1.135605e-01
## prdl.my.fctriPad3
## 4.243587e-02
## sprice.d20nexp
## 4.277500e-01
## startprice.dgt1.is9
## -5.907102e-02
## sprice.log10
## -1.243793e-01
## sprice.root2
## -4.946413e-02
## startprice.dcm1.is9
## -6.894880e-02
## spdiff.cut.fctr(-1,0]:D.chrs.n.log
## 1.100557e-02
## spdiff.cut.fctr(-10,-1]:D.chrs.pnct11.n.log
## 2.837955e-01
## spdiff.cut.fctr(1,10]:D.chrs.pnct11.n.log
## 1.581887e-01
## spdiff.cut.fctr(10,100]:D.chrs.pnct11.n.log
## 4.441550e-02
## spdiff.cut.fctr(-1,0]:D.chrs.uppr.n.log
## 1.145761e-02
## spdiff.cut.fctr(-1,0]:D.terms.post.stem.n.log
## 1.926923e-02
## spdiff.cut.fctr(100,1e+03]:D.terms.post.stem.n.log
## -1.793469e-04
## spdiff.cut.fctr(-1,0]:D.terms.post.stop.n.log
## 1.900714e-02
## spdiff.cut.fctr(100,1e+03]:D.terms.post.stop.n.log
## -1.428824e-04
## spdiff.cut.fctr(-1,0]:D.weight.post.stop.sum
## 1.641529e-02
## spdiff.cut.fctr(-1,0]:D.wrds.n.log
## 1.582613e-02
## spdiff.cut.fctr(-100,-10]:D.wrds.stop.n.log
## 4.094361e-04
## spdiff.cut.fctr(-1,0]:D.wrds.unq.n.log
## 1.934223e-02
## spdiff.cut.fctr(100,1e+03]:D.wrds.unq.n.log
## -8.002001e-05
## spdiff.cut.fctr(-100,-10]:biddable
## 5.585199e-01
## spdiff.cut.fctr(-10,-1]:biddable
## 4.692244e-01
## spdiff.cut.fctr(-1,0]:biddable
## 3.018453e-01
## spdiff.cut.fctr(0,1]:biddable
## 2.658420e-01
## spdiff.cut.fctr(1,10]:biddable
## 6.396551e-01
## spdiff.cut.fctr(10,100]:biddable
## 8.812275e-01
## spdiff.cut.fctr(100,1e+03]:biddable
## 5.699894e-01
## spdiff.cut.fctr(-1,0]:cellular.fctr1
## 3.731648e-02
## spdiff.cut.fctr(0,1]:cellular.fctr1
## 2.264110e-01
## spdiff.cut.fctr(1,10]:cellular.fctr1
## 1.322853e-01
## spdiff.cut.fctr(10,100]:cellular.fctr1
## 1.044013e-01
## spdiff.cut.fctr(-100,-10]:cellular.fctrUnknown
## -9.034152e-02
## spdiff.cut.fctr(-10,-1]:cellular.fctrUnknown
## 2.057604e-01
## spdiff.cut.fctr(100,1e+03]:cellular.fctrUnknown
## -1.171880e-01
## cellular.fctr0:carrier.fctrNone
## 5.002147e-03
## spdiff.cut.fctr(-100,-10]:color.fctrGold
## 1.122160e-01
## spdiff.cut.fctr(1,10]:color.fctrGold
## 3.655952e-01
## spdiff.cut.fctr(-10,-1]:color.fctrUnknown
## 3.780944e-01
## spdiff.cut.fctr(-1,0]:color.fctrUnknown
## 3.742020e-03
## spdiff.cut.fctr(1,10]:color.fctrUnknown
## 2.045982e-03
## spdiff.cut.fctr(10,100]:color.fctrUnknown
## 2.725975e-01
## spdiff.cut.fctr(-10,-1]:condition.fctrManufacturer refurbished
## -1.526723e-03
## spdiff.cut.fctr(-100,-10]:condition.fctrNew other (see details)
## -1.110529e-01
## spdiff.cut.fctr(-10,-1]:condition.fctrNew other (see details)
## -2.928421e-02
## spdiff.cut.fctr(1,10]:condition.fctrNew other (see details)
## 9.826157e-01
## spdiff.cut.fctr(10,100]:condition.fctrNew other (see details)
## 6.948083e-01
## spdiff.cut.fctr(100,1e+03]:condition.fctrNew other (see details)
## 9.550736e-01
## spdiff.cut.fctr(-100,-10]:condition.fctrSeller refurbished
## -1.752041e-01
## spdiff.cut.fctr(-10,-1]:condition.fctrSeller refurbished
## -4.712018e-01
## spdiff.cut.fctr(-100,-10]:prdl.my.fctriPad1
## -2.157974e-01
## spdiff.cut.fctr(-10,-1]:prdl.my.fctriPad1
## 6.975783e-02
## spdiff.cut.fctr(0,1]:prdl.my.fctriPad1
## 3.509679e-01
## spdiff.cut.fctr(10,100]:prdl.my.fctriPad1
## 3.133797e-01
## spdiff.cut.fctr(1,10]:prdl.my.fctriPad2
## 1.717168e-01
## spdiff.cut.fctr(10,100]:prdl.my.fctriPad2
## -2.022383e-01
## spdiff.cut.fctr(-1,0]:prdl.my.fctriPad3
## 1.105046e-01
## spdiff.cut.fctr(1,10]:prdl.my.fctriPad3
## 2.662785e-02
## spdiff.cut.fctr(100,1e+03]:prdl.my.fctriPad3
## -5.636890e-01
## spdiff.cut.fctr(10,100]:prdl.my.fctriPad4
## 6.770895e-01
## spdiff.cut.fctr(-10,-1]:prdl.my.fctriPadAir
## 9.931938e-01
## spdiff.cut.fctr(10,100]:prdl.my.fctriPadAir
## 9.555347e-01
## spdiff.cut.fctr(-100,-10]:prdl.my.fctriPadAir2
## 1.243546e-01
## spdiff.cut.fctr(10,100]:prdl.my.fctriPadAir2
## 4.751418e-01
## spdiff.cut.fctr(100,1e+03]:prdl.my.fctriPadAir2
## 5.966572e-01
## spdiff.cut.fctr(-100,-10]:prdl.my.fctriPadmini
## -4.333316e-02
## spdiff.cut.fctr(-10,-1]:prdl.my.fctriPadmini
## 8.556741e-02
## spdiff.cut.fctr(1,10]:prdl.my.fctriPadmini
## 2.144010e-01
## spdiff.cut.fctr(10,100]:prdl.my.fctriPadmini
## -8.041997e-02
## spdiff.cut.fctr(-10,-1]:prdl.my.fctriPadmini2
## 4.574266e-01
## spdiff.cut.fctr(10,100]:prdl.my.fctriPadmini2
## 7.549910e-01
## spdiff.cut.fctr(-10,-1]:prdl.my.fctriPadmini3
## -2.846370e-02
## prdl.my.fctriPad4:.clusterid.fctr3
## -3.571309e-01
## spdiff.cut.fctr(-100,-10]:sprice.d20nexp
## 2.879553e+00
## spdiff.cut.fctr(-1,0]:sprice.d20nexp
## 1.717869e-01
## spdiff.cut.fctr(0,1]:sprice.d20nexp
## 4.781606e-02
## spdiff.cut.fctr(1,10]:sprice.d20nexp
## 7.414976e-02
## spdiff.cut.fctr(100,1e+03]:sprice.d20nexp
## -1.011472e-01
## spdiff.cut.fctr(-10,-1]:startprice.dgt1.is9
## 4.042733e-02
## spdiff.cut.fctr(0,1]:startprice.dgt1.is9
## 6.316109e-02
## spdiff.cut.fctr(1,10]:startprice.dgt1.is9
## 5.209529e-03
## spdiff.cut.fctr(100,1e+03]:startprice.dgt1.is9
## -3.291644e-01
## spdiff.cut.fctr(-1,0]:startprice.dgt2.is9
## 3.351426e-01
## spdiff.cut.fctr(0,1]:startprice.dgt2.is9
## 3.834696e-01
## spdiff.cut.fctr(10,100]:startprice.dgt2.is9
## 9.905446e-02
## spdiff.cut.fctr(-100,-10]:storage.fctr16
## -1.444992e-01
## spdiff.cut.fctr(-10,-1]:storage.fctr16
## 9.110401e-02
## spdiff.cut.fctr(-1,0]:storage.fctr16
## 1.109884e-01
## spdiff.cut.fctr(0,1]:storage.fctr16
## 2.204076e-01
## spdiff.cut.fctr(1,10]:storage.fctr16
## 2.981404e-01
## spdiff.cut.fctr(10,100]:storage.fctr16
## 1.454966e-01
## spdiff.cut.fctr(100,1e+03]:storage.fctr16
## -3.156438e-01
## spdiff.cut.fctr(-10,-1]:storage.fctr32
## 1.599462e-01
## spdiff.cut.fctr(-100,-10]:storage.fctr64
## 7.494529e-02
## spdiff.cut.fctr(-1,0]:storage.fctr64
## 3.895672e-01
## spdiff.cut.fctr(10,100]:storage.fctr64
## 1.393288e-01
## spdiff.cut.fctr(-10,-1]:storage.fctrUnknown
## 2.059070e-01
## spdiff.cut.fctr(100,1e+03]:storage.fctrUnknown
## -1.411480e-01
## spdiff.cut.fctr(-10,-1]:cellular.fctr0:carrier.fctrNone
## 2.254737e-01
## spdiff.cut.fctr(-1,0]:cellular.fctr0:carrier.fctrNone
## 2.651200e-01
## spdiff.cut.fctr(1,10]:cellular.fctr0:carrier.fctrNone
## 2.417793e-01
## spdiff.cut.fctr(10,100]:cellular.fctr0:carrier.fctrNone
## 9.286267e-02
## spdiff.cut.fctr(100,1e+03]:cellular.fctr0:carrier.fctrNone
## -3.404189e-02
## spdiff.cut.fctr(-100,-10]:cellular.fctr1:carrier.fctrSprint
## 3.336623e-01
## spdiff.cut.fctr(-10,-1]:cellular.fctr1:carrier.fctrT-Mobile
## 4.634148e-01
## spdiff.cut.fctr(-100,-10]:cellular.fctr1:carrier.fctrUnknown
## -1.889200e-01
## spdiff.cut.fctr(1,10]:cellular.fctr1:carrier.fctrUnknown
## 4.433612e-01
## spdiff.cut.fctr(100,1e+03]:cellular.fctr1:carrier.fctrUnknown
## 4.471624e-01
## spdiff.cut.fctr(-100,-10]:cellular.fctrUnknown:carrier.fctrUnknown
## -9.014235e-02
## spdiff.cut.fctr(-10,-1]:cellular.fctrUnknown:carrier.fctrUnknown
## 2.051578e-01
## spdiff.cut.fctr(100,1e+03]:cellular.fctrUnknown:carrier.fctrUnknown
## -1.168833e-01
## spdiff.cut.fctr(-10,-1]:prdl.my.fctriPad1:.clusterid.fctr2
## 3.276400e-01
## spdiff.cut.fctr(1,10]:prdl.my.fctriPad1:.clusterid.fctr2
## -2.976289e-01
## spdiff.cut.fctr(-1,0]:prdl.my.fctriPad3:.clusterid.fctr2
## 3.345650e-01
## spdiff.cut.fctr(-100,-10]:prdl.my.fctriPad4:.clusterid.fctr2
## 1.274939e-03
## spdiff.cut.fctr(1,10]:prdl.my.fctriPadmini2:.clusterid.fctr2
## -1.109086e+00
## spdiff.cut.fctr(10,100]:prdl.my.fctriPadmini2:.clusterid.fctr2
## 1.149500e-01
## spdiff.cut.fctr(-100,-10]:prdl.my.fctrUnknown:.clusterid.fctr3
## -1.987346e-01
## spdiff.cut.fctr(1,10]:prdl.my.fctrUnknown:.clusterid.fctr3
## 1.129566e+00
## spdiff.cut.fctr(100,1e+03]:prdl.my.fctrUnknown:.clusterid.fctr3
## -7.576486e-02
## spdiff.cut.fctr(1,10]:prdl.my.fctriPad1:.clusterid.fctr3
## -4.385553e-01
## spdiff.cut.fctr(10,100]:prdl.my.fctriPad1:.clusterid.fctr3
## 8.879799e-02
## spdiff.cut.fctr(-100,-10]:prdl.my.fctriPad4:.clusterid.fctr3
## -2.484035e-02
## spdiff.cut.fctr(-10,-1]:prdl.my.fctriPad4:.clusterid.fctr3
## -2.685868e-02
## sold.fctr sold.fctr.predict.CSM.X.Interact.glmnet.N
## 1 N 470
## 2 Y 57
## sold.fctr.predict.CSM.X.Interact.glmnet.Y
## 1 78
## 2 415
## Prediction
## Reference N Y
## N 470 78
## Y 57 415
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.676471e-01 7.346350e-01 8.452972e-01 8.878492e-01 5.372549e-01
## AccuracyPValue McnemarPValue
## 6.513110e-113 8.519170e-02
## sold.fctr sold.fctr.predict.CSM.X.Interact.glmnet.N
## 1 N 355
## 2 Y 73
## sold.fctr.predict.CSM.X.Interact.glmnet.Y
## 1 92
## 2 310
## Prediction
## Reference N Y
## N 355 92
## Y 73 310
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.012048e-01 6.014470e-01 7.724022e-01 8.278495e-01 5.385542e-01
## AccuracyPValue McnemarPValue
## 2.036127e-56 1.611249e-01
## id
## 1 CSM.X.Interact.glmnet
## feats
## 1 .rnorm,D.weight.sum,D.weight.post.stem.sum,D.chrs.pnct13.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,startprice.dcm2.is9,sprice.root2,sprice.log10,startprice.dcm1.is9,D.weight.sum.stem.stop.Ratio,spdiff.cut.fctr*biddable,spdiff.cut.fctr*storage.fctr,spdiff.cut.fctr*prdl.my.fctr,spdiff.cut.fctr*cellular.fctr,spdiff.cut.fctr*D.chrs.pnct11.n.log,spdiff.cut.fctr*condition.fctr,spdiff.cut.fctr*color.fctr,spdiff.cut.fctr*sprice.d20nexp,spdiff.cut.fctr*startprice.dgt2.is9,spdiff.cut.fctr*D.wrds.n.log,spdiff.cut.fctr*D.chrs.n.log,spdiff.cut.fctr*D.chrs.uppr.n.log,spdiff.cut.fctr*D.terms.post.stem.n.log,spdiff.cut.fctr*D.terms.post.stop.n.log,spdiff.cut.fctr*D.wrds.stop.n.log,spdiff.cut.fctr*D.wrds.unq.n.log,spdiff.cut.fctr*startprice.dgt1.is9,spdiff.cut.fctr*D.weight.post.stop.sum,spdiff.cut.fctr*cellular.fctr:carrier.fctr,spdiff.cut.fctr*prdl.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 39.004 2.421
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8557466 0.9233577 0.7881356 0.9302201
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.8601036 0.8316958
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8452972 0.8878492 0.6584576
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.7820106 0.8590604 0.7049608 0.8636807
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.4 0.7898089 0.8012048
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7724022 0.8278495 0.601447
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01558676 0.0323943# Check if other preProcess methods improve model performance
fit.models_1_chunk_df <-
myadd_chunk(fit.models_1_chunk_df, "fit.models_1_preProc", major.inc = FALSE,
label.minor = "preProc")## label step_major step_minor label_minor bgn
## 19 fit.models_1_CSM.X.Interact 1 18 glmnet 833.038
## 20 fit.models_1_preProc 1 19 preProc 877.175
## end elapsed
## 19 877.175 44.137
## 20 NA NAmdl_id <- orderBy(get_model_sel_frmla(), glb_models_df)[1, "id"]
indep_vars_vctr <- trim(unlist(strsplit(glb_models_df[glb_models_df$id == mdl_id,
"feats"], "[,]")))
method <- tail(unlist(strsplit(mdl_id, "[.]")), 1)
mdl_id_pfx <- paste0(head(unlist(strsplit(mdl_id, "[.]")), -1), collapse = ".")
if (!is.null(glbObsFitOutliers[[mdl_id_pfx]])) {
fitobs_df <- glb_fitobs_df[!(glb_fitobs_df[, glb_id_var] %in%
glbObsFitOutliers[[mdl_id_pfx]]), ]
} else fitobs_df <- glb_fitobs_df
for (prePr in glb_preproc_methods) {
# The operations are applied in this order:
# Box-Cox/Yeo-Johnson transformation, centering, scaling, range, imputation, PCA, ICA then spatial sign.
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix=mdl_id_pfx,
type=glb_model_type, tune.df=glb_tune_models_df,
trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds,
trainControl.repeats=glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method=method, train.preProcess=prePr)),
indep_vars=indep_vars_vctr, rsp_var=glb_rsp_var,
fit_df=fitobs_df, OOB_df=glb_OOBobs_df)
} ## [1] "fitting model: CSM.X.YeoJohnson.bagEarth"
## [1] " indep_vars: spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr"
## + : degree=1, nprune=64## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## earth glm Y: did not converge after 25 iterations## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## - : degree=1, nprune=64
## Aggregating results
## Fitting final model on full training set## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred##
## Call:
## bagEarth.default(x = structure(c(-0.486060981063258, 1.39274881703059, 2.41546282774433, -1.6928748576698, -0.917940471159667, 0.364367245695126, 1.50008799123203, -0.371596417154409, -0.488451789070766, -0.4642380177469, 0.877509112004972, 1.24404379822377, -0.528755598675093, 1.06432809212002, 1.80780895487277, -0.832443370059908, 0.0808673029265311, 1.0096537965188, -1.72015812741148, -0.509956173142891, -0.933920260127077, -1.81924388255596, -0.388252360677581, 0.0316421965659803, 1.1338480035998, -0.792627879074597, -1.30261055024983, 0.073672769734936, -1.0792465317048, 0.538831908580791, 0.97728401753606, 0.626714863796594, 0.375739786007143, -0.692246787413592, 1.32154142473581, -1.05670313060523, -0.902361591204141, -0.705818073751696, 1.06207654920172, -0.756474919866002, -0.246132959592076, -0.738131275945242, 0.770826625476836, -0.560971640179677, -0.567576991665449, 1.15405075274841, 0.418355389363326, 0.333757673844418, 1.38315210048006, -0.882386729856467, 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"1630", "1631", "1632", "1634", "1637", "1638", "1640", "1645", "1648", "1650", "1652", "1653", "1657", "1658", "1660", "1661", "1662", "1663", "1668", "1669", "1670", "1671", "1673", "1677", "1678", "1680", "1681", "1682", "1683", "1685", "1688", "1693", "1697", "1700", "1701", "1702", "1704", "1706", "1708", "1709", "1710", "1711", "1713", "1717", "1718", "1723", "1724", "1725", "1726", "1730", "1731", "1732", "1733", "1734", "1736", "1737", "1740", "1744", "1745", "1746", "1747", "1748", "1749", "1751", "1753", "1754", "1755", "1759", "1760", "1761", "1762", "1768", "1769", "1774", "1776", "1777", "1778", "1779", "1780", "1782", "1791", "1792", "1793", "1794", "1797", "1799", "1803", "1804", "1805", "1808", "1809", "1813", "1816", "1817", "1822", "1823", "1824", "1827", "1828", "1829", "1830", "1834", "1835", "1836", "1838", "1839", "1840", "1842", "1843", "1845", "1846", "1847", "1848", "1850")), degree = 1, nprune = 64, keepxy = TRUE, glm = structure(list( family = function (link = "logit") { linktemp <- substitute(link) if (!is.character(linktemp)) linktemp <- deparse(linktemp) okLinks <- c("logit", "probit", "cloglog", "cauchit", "log") if (linktemp %in% okLinks) stats <- make.link(linktemp) else if (is.character(link)) { stats <- make.link(link) linktemp <- link } else { if (inherits(link, "link-glm")) { stats <- link if (!is.null(stats$name)) linktemp <- stats$name } else { stop(gettextf("link \"%s\" not available for binomial family; available links are %s", linktemp, paste(sQuote(okLinks), collapse = ", ")), domain = NA) } } variance <- function(mu) mu * (1 - mu) validmu <- function(mu) all(is.finite(mu)) && all(mu > 0 & mu < 1) dev.resids <- function(y, mu, wt) .Call(C_binomial_dev_resids, y, mu, wt) aic <- function(y, n, mu, wt, dev) { m <- if (any(n > 1)) n else wt -2 * sum(ifelse(m > 0, (wt/m), 0) * dbinom(round(m * y), round(m), mu, log = TRUE)) } initialize <- expression({ if (NCOL(y) == 1) { if (is.factor(y)) y <- y != levels(y)[1L] n <- rep.int(1, nobs) y[weights == 0] <- 0 if (any(y < 0 | y > 1)) stop("y values must be 0 <= y <= 1") mustart <- (weights * y + 0.5)/(weights + 1) m <- weights * y if (any(abs(m - round(m)) > 0.001)) warning("non-integer #successes in a binomial glm!") } else if (NCOL(y) == 2) { if (any(abs(y - round(y)) > 0.001)) warning("non-integer counts in a binomial glm!") n <- y[, 1] + y[, 2] y <- ifelse(n == 0, 0, y[, 1]/n) weights <- weights * n mustart <- (n * y + 0.5)/(n + 1) } else stop("for the 'binomial' family, y must be a vector of 0 and 1's\nor a 2 column matrix where col 1 is no. successes and col 2 is no. failures") }) simfun <- function(object, nsim) { ftd <- fitted(object) n <- length(ftd) ntot <- n * nsim wts <- object$prior.weights if (any(wts%%1 != 0)) stop("cannot simulate from non-integer prior.weights") if (!is.null(m <- object$model)) { y <- model.response(m) if (is.factor(y)) { yy <- factor(1 + rbinom(ntot, size = 1, prob = ftd), labels = levels(y)) split(yy, rep(seq_len(nsim), each = n)) } else if (is.matrix(y) && ncol(y) == 2) { yy <- vector("list", nsim) for (i in seq_len(nsim)) { Y <- rbinom(n, size = wts, prob = ftd) YY <- cbind(Y, wts - Y) colnames(YY) <- colnames(y) yy[[i]] <- YY } yy } else rbinom(ntot, size = wts, prob = ftd)/wts } else rbinom(ntot, size = wts, prob = ftd)/wts } structure(list(family = "binomial", link = linktemp, linkfun = stats$linkfun, linkinv = stats$linkinv, variance = variance, dev.resids = dev.resids, aic = aic, mu.eta = stats$mu.eta, initialize = initialize, validmu = validmu, valideta = stats$valideta, simulate = simfun), class = "family") }), .Names = "family"))
##
## Out of bag statistics:
##
## Accuracy Kappa
## 0% 0.7664 0.5315
## 2.5% 0.7729 0.5430
## 25% 0.7863 0.5702
## 50% 0.8037 0.6025
## 75% 0.8159 0.6277
## 97.5% 0.8325 0.6639
## 100% 0.8400 0.6800
##
## Model Selection Statistics:
##
## Num Terms Num Variables
## Length:1544 Length:1544
## Class :character Class :character
## Mode :character Mode :character
## sold.fctr sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.N
## 1 N 503
## 2 Y 71
## sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.Y
## 1 45
## 2 399
## Prediction
## Reference N Y
## N 503 45
## Y 71 399
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.860511e-01 7.698502e-01 8.649151e-01 9.049229e-01 5.383104e-01
## AccuracyPValue McnemarPValue
## 8.609242e-127 2.027650e-02
## sold.fctr sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.N
## 1 N 354
## 2 Y 73
## sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.Y
## 1 93
## 2 310
## Prediction
## Reference N Y
## N 354 93
## Y 73 310
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.000000e-01 5.991062e-01 7.711403e-01 8.267105e-01 5.385542e-01
## AccuracyPValue McnemarPValue
## 7.010366e-56 1.402970e-01
## id
## 1 CSM.X.YeoJohnson.bagEarth
## feats
## 1 spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 116.682 57.979
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8834097 0.9178832 0.8489362 0.9574934
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.8730853 0.8010417
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8649151 0.9049229 0.5969627
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.8060701 0.852349 0.7597911 0.8782951
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.4 0.7888041 0.8
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7711403 0.8267105 0.5991062
## [1] "fitting model: CSM.X.center.scale.bagEarth"
## [1] " indep_vars: spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr"
## + : degree=1, nprune=64## Warning in preProcess.default(thresh = 0.95, k = 5, method
## = c("center", : These variables have zero variances:
## cellular.fctr1:carrier.fctrNone, cellular.fctrUnknown:carrier.fctrNone,
## cellular.fctr0:carrier.fctrOther, cellular.fctr1:carrier.fctrOther,
## cellular.fctrUnknown:carrier.fctrOther, cellular.fctr0:carrier.fctrSprint,
## cellular.fctrUnknown:carrier.fctrSprint, cellular.fctr0:carrier.fctrT-
## Mobile, cellular.fctrUnknown:carrier.fctrT-Mobile,
## cellular.fctr0:carrier.fctrUnknown, cellular.fctr0:carrier.fctrVerizon,
## cellular.fctrUnknown:carrier.fctrVerizon,
## prdl.my.fctriPad2:.clusterid.fctr2, prdl.my.fctriPadAir2:.clusterid.fctr2,
## prdl.my.fctriPadmini:.clusterid.fctr2,
## prdl.my.fctriPadmini3:.clusterid.fctr2, prdl.my.fctriPad2:.clusterid.fctr3,
## prdl.my.fctriPad3:.clusterid.fctr3, prdl.my.fctriPadAir:.clusterid.fctr3,
## prdl.my.fctriPadAir2:.clusterid.fctr3,
## prdl.my.fctriPadmini:.clusterid.fctr3,
## prdl.my.fctriPadmini2:.clusterid.fctr3,
## prdl.my.fctriPadmini3:.clusterid.fctr3## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## - : degree=1, nprune=64
## Aggregating results
## Fitting final model on full training set## Warning in preProcess.default(thresh = 0.95, k = 5, method
## = c("center", : These variables have zero variances:
## cellular.fctr1:carrier.fctrNone, cellular.fctrUnknown:carrier.fctrNone,
## cellular.fctr0:carrier.fctrOther, cellular.fctr1:carrier.fctrOther,
## cellular.fctrUnknown:carrier.fctrOther, cellular.fctr0:carrier.fctrSprint,
## cellular.fctrUnknown:carrier.fctrSprint, cellular.fctr0:carrier.fctrT-
## Mobile, cellular.fctrUnknown:carrier.fctrT-Mobile,
## cellular.fctr0:carrier.fctrUnknown, cellular.fctr0:carrier.fctrVerizon,
## cellular.fctrUnknown:carrier.fctrVerizon,
## prdl.my.fctriPad2:.clusterid.fctr2, prdl.my.fctriPadAir2:.clusterid.fctr2,
## prdl.my.fctriPadmini:.clusterid.fctr2,
## prdl.my.fctriPadmini3:.clusterid.fctr2, prdl.my.fctriPad2:.clusterid.fctr3,
## prdl.my.fctriPad3:.clusterid.fctr3, prdl.my.fctriPadAir:.clusterid.fctr3,
## prdl.my.fctriPadAir2:.clusterid.fctr3,
## prdl.my.fctriPadmini:.clusterid.fctr3,
## prdl.my.fctriPadmini2:.clusterid.fctr3,
## prdl.my.fctriPadmini3:.clusterid.fctr3## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## earth glm Y: did not converge after 25 iterations## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## earth glm Y: did not converge after 25 iterations## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
##
## Call:
## bagEarth.default(x = structure(c(-0.456118496384399, 1.45509318569684, 2.54306883372874, -1.6111942791378, -0.874067991903045, 0.390161693883373, 1.56812413874677, -0.344240175737629, -0.458449840249712, -0.434828253442312, 0.917078733230863, 1.29901576229275, -0.497718967310423, 1.11123256651457, 1.89376809267947, -0.791800745838271, 0.103893909466579, 1.05429683840789, -1.63690331800463, -0.479409694144368, -0.889420538018271, -1.73014998369183, -0.360552366566085, 0.0546116363706654, 1.18375997681691, -0.753414843409819, -1.24174437942992, 0.0966825998815906, -1.02871701861712, 0.568160887416709, 1.02063227845715, 0.658293683416006, 0.401724905286471, -0.656417976056839, 1.38027946991802, -1.0071464412781, -0.859093567825372, -0.669550627630097, 1.10888607164358, -0.718517602895892, -0.220985857596555, -0.700795378340332, 0.806724436166772, -0.529064955831525, -0.535487286198502, 1.20486385825664, 0.445105529419218, 0.359067672305198, 1.44500243885285, -0.839883440769898, 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"1065", "1067", "1068", "1069", "1070", "1071", "1074", "1075", "1087", "1088", "1091", "1092", "1093", "1096", "1097", "1099", "1100", "1102", "1103", "1104", "1105", "1106", "1113", "1114", "1115", "1116", "1118", "1121", "1122", "1123", "1124", "1125", "1126", "1127", "1128", "1129", "1130", "1132", "1133", "1134", "1135", "1137", "1138", "1140", "1142", "1144", "1148", "1149", "1150", "1154", "1156", "1160", "1164", "1166", "1169", "1170", "1171", "1173", "1174", "1175", "1176", "1177", "1179", "1180", "1183", "1184", "1188", "1189", "1190", "1191", "1194", "1195", "1198", "1200", "1201", "1204", "1205", "1206", "1207", "1208", "1211", "1212", "1213", "1215", "1216", "1220", "1221", "1222", "1223", "1228", "1230", "1232", "1234", "1235", "1236", "1237", "1239", "1240", "1241", "1243", "1244", "1246", "1247", "1250", "1251", "1253", "1257", "1258", "1262", "1263", "1264", "1265", "1266", "1267", "1269", "1270", "1274", "1276", "1281", "1283", "1284", "1289", "1291", "1296", "1299", "1304", "1307", "1308", "1310", "1312", "1313", "1316", "1317", "1318", "1321", "1322", "1323", "1325", "1326", "1329", "1330", "1332", "1333", "1334", "1336", "1337", "1338", "1339", "1340", "1344", "1345", "1347", "1349", "1352", "1356", "1357", "1359", "1360", "1363", "1365", "1366", "1367", "1368", "1369", "1370", "1371", "1372", "1374", "1375", "1376", "1380", "1381", "1385", "1386", "1387", "1389", "1390", "1392", "1394", "1395", "1398", "1399", "1400", "1403", "1404", "1405", "1406", "1410", "1417", "1418", "1419", "1420", "1421", "1425", "1426", "1427", "1428", "1429", "1433", "1434", "1435", "1436", "1437", "1438", "1439", "1444", "1445", "1446", "1448", "1449", "1450", "1451", "1452", "1454", "1455", "1456", "1457", "1460", "1462", "1463", "1465", "1466", "1468", "1472", "1473", "1474", "1476", "1478", "1479", "1480", "1481", "1483", "1485", "1487", "1488", "1490", "1491", "1492", "1493", "1494", "1496", "1497", "1498", "1500", "1501", "1502", "1503", "1504", "1505", "1508", "1509", "1514", "1515", "1516", "1518", "1519", "1521", "1524", "1525", "1526", "1528", "1529", "1530", "1531", "1532", "1534", "1535", "1537", "1539", "1540", "1541", "1545", "1546", "1550", "1552", "1553", "1555", "1557", "1558", "1559", "1561", "1562", "1564", "1569", "1570", "1574", "1575", "1576", "1579", "1581", "1584", "1585", "1586", "1587", "1589", "1591", "1595", "1599", "1600", "1602", "1604", "1605", "1606", "1607", "1609", "1610", "1615", "1616", "1620", "1624", "1625", "1630", "1631", "1632", "1634", "1637", "1638", "1640", "1645", "1648", "1650", "1652", "1653", "1657", "1658", "1660", "1661", "1662", "1663", "1668", "1669", "1670", "1671", "1673", "1677", "1678", "1680", "1681", "1682", "1683", "1685", "1688", "1693", "1697", "1700", "1701", "1702", "1704", "1706", "1708", "1709", "1710", "1711", "1713", "1717", "1718", "1723", "1724", "1725", "1726", "1730", "1731", "1732", "1733", "1734", "1736", "1737", "1740", "1744", "1745", "1746", "1747", "1748", "1749", "1751", "1753", "1754", "1755", "1759", "1760", "1761", "1762", "1768", "1769", "1774", "1776", "1777", "1778", "1779", "1780", "1782", "1791", "1792", "1793", "1794", "1797", "1799", "1803", "1804", "1805", "1808", "1809", "1813", "1816", "1817", "1822", "1823", "1824", "1827", "1828", "1829", "1830", "1834", "1835", "1836", "1838", "1839", "1840", "1842", "1843", "1845", "1846", "1847", "1848", "1850")), degree = 1, nprune = 64, keepxy = TRUE, glm = structure(list( family = function (link = "logit") { linktemp <- substitute(link) if (!is.character(linktemp)) linktemp <- deparse(linktemp) okLinks <- c("logit", "probit", "cloglog", "cauchit", "log") if (linktemp %in% okLinks) stats <- make.link(linktemp) else if (is.character(link)) { stats <- make.link(link) linktemp <- link } else { if (inherits(link, "link-glm")) { stats <- link if (!is.null(stats$name)) linktemp <- stats$name } else { stop(gettextf("link \"%s\" not available for binomial family; available links are %s", linktemp, paste(sQuote(okLinks), collapse = ", ")), domain = NA) } } variance <- function(mu) mu * (1 - mu) validmu <- function(mu) all(is.finite(mu)) && all(mu > 0 & mu < 1) dev.resids <- function(y, mu, wt) .Call(C_binomial_dev_resids, y, mu, wt) aic <- function(y, n, mu, wt, dev) { m <- if (any(n > 1)) n else wt -2 * sum(ifelse(m > 0, (wt/m), 0) * dbinom(round(m * y), round(m), mu, log = TRUE)) } initialize <- expression({ if (NCOL(y) == 1) { if (is.factor(y)) y <- y != levels(y)[1L] n <- rep.int(1, nobs) y[weights == 0] <- 0 if (any(y < 0 | y > 1)) stop("y values must be 0 <= y <= 1") mustart <- (weights * y + 0.5)/(weights + 1) m <- weights * y if (any(abs(m - round(m)) > 0.001)) warning("non-integer #successes in a binomial glm!") } else if (NCOL(y) == 2) { if (any(abs(y - round(y)) > 0.001)) warning("non-integer counts in a binomial glm!") n <- y[, 1] + y[, 2] y <- ifelse(n == 0, 0, y[, 1]/n) weights <- weights * n mustart <- (n * y + 0.5)/(n + 1) } else stop("for the 'binomial' family, y must be a vector of 0 and 1's\nor a 2 column matrix where col 1 is no. successes and col 2 is no. failures") }) simfun <- function(object, nsim) { ftd <- fitted(object) n <- length(ftd) ntot <- n * nsim wts <- object$prior.weights if (any(wts%%1 != 0)) stop("cannot simulate from non-integer prior.weights") if (!is.null(m <- object$model)) { y <- model.response(m) if (is.factor(y)) { yy <- factor(1 + rbinom(ntot, size = 1, prob = ftd), labels = levels(y)) split(yy, rep(seq_len(nsim), each = n)) } else if (is.matrix(y) && ncol(y) == 2) { yy <- vector("list", nsim) for (i in seq_len(nsim)) { Y <- rbinom(n, size = wts, prob = ftd) YY <- cbind(Y, wts - Y) colnames(YY) <- colnames(y) yy[[i]] <- YY } yy } else rbinom(ntot, size = wts, prob = ftd)/wts } else rbinom(ntot, size = wts, prob = ftd)/wts } structure(list(family = "binomial", link = linktemp, linkfun = stats$linkfun, linkinv = stats$linkinv, variance = variance, dev.resids = dev.resids, aic = aic, mu.eta = stats$mu.eta, initialize = initialize, validmu = validmu, valideta = stats$valideta, simulate = simfun), class = "family") }), .Names = "family"))
##
## Out of bag statistics:
##
## Accuracy Kappa
## 0% 0.7540 0.5056
## 2.5% 0.7605 0.5137
## 25% 0.7902 0.5791
## 50% 0.7984 0.5939
## 75% 0.8137 0.6253
## 97.5% 0.8332 0.6665
## 100% 0.8391 0.6780
##
## Model Selection Statistics:
##
## Num Terms Num Variables
## Length:1523 Length:1523
## Class :character Class :character
## Mode :character Mode :character
## sold.fctr sold.fctr.predict.CSM.X.center.scale.bagEarth.N
## 1 N 505
## 2 Y 70
## sold.fctr.predict.CSM.X.center.scale.bagEarth.Y
## 1 43
## 2 400
## Prediction
## Reference N Y
## N 505 43
## Y 70 400
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.889980e-01 7.757683e-01 8.680742e-01 9.076395e-01 5.383104e-01
## AccuracyPValue McnemarPValue
## 2.794828e-129 1.445014e-02
## sold.fctr sold.fctr.predict.CSM.X.center.scale.bagEarth.N
## 1 N 383
## 2 Y 93
## sold.fctr.predict.CSM.X.center.scale.bagEarth.Y
## 1 64
## 2 290
## Prediction
## Reference N Y
## N 383 64
## Y 93 290
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.108434e-01 6.173498e-01 7.825114e-01 8.369472e-01 5.385542e-01
## AccuracyPValue McnemarPValue
## 7.840686e-61 2.544078e-02
## id
## 1 CSM.X.center.scale.bagEarth
## feats
## 1 spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 124.921 62.555
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8862983 0.9215328 0.8510638 0.9583825
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.8762322 0.7965221
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8680742 0.9076395 0.590061
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.8070017 0.8568233 0.7571802 0.8764143
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.7869742 0.8108434
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7825114 0.8369472 0.6173498
## [1] "fitting model: CSM.X.range.bagEarth"
## [1] " indep_vars: spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : myfit_mdl: preProcess method:
## range currently does not work for columns with no variance:
## cellular.fctr1:carrier.fctrNone, cellular.fctrUnknown:carrier.fctrNone,
## cellular.fctr0:carrier.fctrOther, cellular.fctr1:carrier.fctrOther,
## cellular.fctrUnknown:carrier.fctrOther, cellular.fctr0:carrier.fctrSprint,
## cellular.fctrUnknown:carrier.fctrSprint, cellular.fctr0:carrier.fctrT-
## Mobile, cellular.fctrUnknown:carrier.fctrT-Mobile,
## cellular.fctr0:carrier.fctrUnknown, cellular.fctr0:carrier.fctrVerizon,
## cellular.fctrUnknown:carrier.fctrVerizon,
## prdl.my.fctriPad2:.clusterid.fctr2, prdl.my.fctriPadAir2:.clusterid.fctr2,
## prdl.my.fctriPadmini:.clusterid.fctr2,
## prdl.my.fctriPadmini3:.clusterid.fctr2, prdl.my.fctriPad2:.clusterid.fctr3,
## prdl.my.fctriPad3:.clusterid.fctr3, prdl.my.fctriPadAir:.clusterid.fctr3,
## prdl.my.fctriPadAir2:.clusterid.fctr3,
## prdl.my.fctriPadmini:.clusterid.fctr3,
## prdl.my.fctriPadmini2:.clusterid.fctr3,
## prdl.my.fctriPadmini3:.clusterid.fctr3## [1] "fitting model: CSM.X.pca.bagEarth"
## [1] " indep_vars: spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : myfit_mdl: preProcess method:
## range currently does not work for columns with no variance:
## cellular.fctr1:carrier.fctrNone, cellular.fctrUnknown:carrier.fctrNone,
## cellular.fctr0:carrier.fctrOther, cellular.fctr1:carrier.fctrOther,
## cellular.fctrUnknown:carrier.fctrOther, cellular.fctr0:carrier.fctrSprint,
## cellular.fctrUnknown:carrier.fctrSprint, cellular.fctr0:carrier.fctrT-
## Mobile, cellular.fctrUnknown:carrier.fctrT-Mobile,
## cellular.fctr0:carrier.fctrUnknown, cellular.fctr0:carrier.fctrVerizon,
## cellular.fctrUnknown:carrier.fctrVerizon,
## prdl.my.fctriPad2:.clusterid.fctr2, prdl.my.fctriPadAir2:.clusterid.fctr2,
## prdl.my.fctriPadmini:.clusterid.fctr2,
## prdl.my.fctriPadmini3:.clusterid.fctr2, prdl.my.fctriPad2:.clusterid.fctr3,
## prdl.my.fctriPad3:.clusterid.fctr3, prdl.my.fctriPadAir:.clusterid.fctr3,
## prdl.my.fctriPadAir2:.clusterid.fctr3,
## prdl.my.fctriPadmini:.clusterid.fctr3,
## prdl.my.fctriPadmini2:.clusterid.fctr3,
## prdl.my.fctriPadmini3:.clusterid.fctr3## [1] "fitting model: CSM.X.ica.bagEarth"
## [1] " indep_vars: spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : myfit_mdl: preProcess method:
## range currently does not work for columns with no variance:
## cellular.fctr1:carrier.fctrNone, cellular.fctrUnknown:carrier.fctrNone,
## cellular.fctr0:carrier.fctrOther, cellular.fctr1:carrier.fctrOther,
## cellular.fctrUnknown:carrier.fctrOther, cellular.fctr0:carrier.fctrSprint,
## cellular.fctrUnknown:carrier.fctrSprint, cellular.fctr0:carrier.fctrT-
## Mobile, cellular.fctrUnknown:carrier.fctrT-Mobile,
## cellular.fctr0:carrier.fctrUnknown, cellular.fctr0:carrier.fctrVerizon,
## cellular.fctrUnknown:carrier.fctrVerizon,
## prdl.my.fctriPad2:.clusterid.fctr2, prdl.my.fctriPadAir2:.clusterid.fctr2,
## prdl.my.fctriPadmini:.clusterid.fctr2,
## prdl.my.fctriPadmini3:.clusterid.fctr2, prdl.my.fctriPad2:.clusterid.fctr3,
## prdl.my.fctriPad3:.clusterid.fctr3, prdl.my.fctriPadAir:.clusterid.fctr3,
## prdl.my.fctriPadAir2:.clusterid.fctr3,
## prdl.my.fctriPadmini:.clusterid.fctr3,
## prdl.my.fctriPadmini2:.clusterid.fctr3,
## prdl.my.fctriPadmini3:.clusterid.fctr3## [1] "fitting model: CSM.X.spatialSign.bagEarth"
## [1] " indep_vars: spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr"
## + : degree=1, nprune=64## Warning in preProcess.default(thresh = 0.95, k = 5, method
## = "spatialSign", : These variables have zero variances:
## cellular.fctr1:carrier.fctrNone, cellular.fctrUnknown:carrier.fctrNone,
## cellular.fctr0:carrier.fctrOther, cellular.fctr1:carrier.fctrOther,
## cellular.fctrUnknown:carrier.fctrOther, cellular.fctr0:carrier.fctrSprint,
## cellular.fctrUnknown:carrier.fctrSprint, cellular.fctr0:carrier.fctrT-
## Mobile, cellular.fctrUnknown:carrier.fctrT-Mobile,
## cellular.fctr0:carrier.fctrUnknown, cellular.fctr0:carrier.fctrVerizon,
## cellular.fctrUnknown:carrier.fctrVerizon,
## prdl.my.fctriPad2:.clusterid.fctr2, prdl.my.fctriPadAir2:.clusterid.fctr2,
## prdl.my.fctriPadmini:.clusterid.fctr2,
## prdl.my.fctriPadmini3:.clusterid.fctr2, prdl.my.fctriPad2:.clusterid.fctr3,
## prdl.my.fctriPad3:.clusterid.fctr3, prdl.my.fctriPadAir:.clusterid.fctr3,
## prdl.my.fctriPadAir2:.clusterid.fctr3,
## prdl.my.fctriPadmini:.clusterid.fctr3,
## prdl.my.fctriPadmini2:.clusterid.fctr3,
## prdl.my.fctriPadmini3:.clusterid.fctr3## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## - : degree=1, nprune=64
## Aggregating results
## Fitting final model on full training set## Warning in preProcess.default(thresh = 0.95, k = 5, method
## = "spatialSign", : These variables have zero variances:
## cellular.fctr1:carrier.fctrNone, cellular.fctrUnknown:carrier.fctrNone,
## cellular.fctr0:carrier.fctrOther, cellular.fctr1:carrier.fctrOther,
## cellular.fctrUnknown:carrier.fctrOther, cellular.fctr0:carrier.fctrSprint,
## cellular.fctrUnknown:carrier.fctrSprint, cellular.fctr0:carrier.fctrT-
## Mobile, cellular.fctrUnknown:carrier.fctrT-Mobile,
## cellular.fctr0:carrier.fctrUnknown, cellular.fctr0:carrier.fctrVerizon,
## cellular.fctrUnknown:carrier.fctrVerizon,
## prdl.my.fctriPad2:.clusterid.fctr2, prdl.my.fctriPadAir2:.clusterid.fctr2,
## prdl.my.fctriPadmini:.clusterid.fctr2,
## prdl.my.fctriPadmini3:.clusterid.fctr2, prdl.my.fctriPad2:.clusterid.fctr3,
## prdl.my.fctriPad3:.clusterid.fctr3, prdl.my.fctriPadAir:.clusterid.fctr3,
## prdl.my.fctriPadAir2:.clusterid.fctr3,
## prdl.my.fctriPadmini:.clusterid.fctr3,
## prdl.my.fctriPadmini2:.clusterid.fctr3,
## prdl.my.fctriPadmini3:.clusterid.fctr3## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
##
## Call:
## bagEarth.default(x = structure(c(-0.0700598324780138, 0.136287394527869, 0.34602883412232, -0.162743668888924, -0.107616838711845, 0.0533467895486048, 0.161048550289795, -0.0413654627950684, -0.0374089872804587, -0.0491186893548325, 0.117315243455885, 0.147713234264911, -0.0737912643560141, 0.13145306918799, 0.204887202882008, -0.0972644177062529, 0.00976342303015051, 0.0887756382927227, -0.139331365790961, -0.054705728327361, -0.115174339938668, -0.266540734411153, -0.0472788005502491, 0.00671270692718855, 0.168264496085931, -0.0794557292923549, -0.16700571533336, 0.0150439136925377, -0.135251291723884, 0.075355398021063, 0.11599696006488, 0.066553079260974, 0.0652897980993443, -0.108191392542189, 0.177238909409064, -0.099252376677147, -0.0809559527189113, -0.115937254255744, 0.168791953647992, -0.0559875551844286, -0.0278982034044906, -0.108916571382117, 0.0956039970605568, -0.0760046673933142, -0.0752832571755808, 0.163327353559107, 0.0494887814662641, 0.0514352576305571, 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"1782", "1791", "1792", "1793", "1794", "1797", "1799", "1803", "1804", "1805", "1808", "1809", "1813", "1816", "1817", "1822", "1823", "1824", "1827", "1828", "1829", "1830", "1834", "1835", "1836", "1838", "1839", "1840", "1842", "1843", "1845", "1846", "1847", "1848", "1850")), degree = 1, nprune = 64, keepxy = TRUE, glm = structure(list( family = function (link = "logit") { linktemp <- substitute(link) if (!is.character(linktemp)) linktemp <- deparse(linktemp) okLinks <- c("logit", "probit", "cloglog", "cauchit", "log") if (linktemp %in% okLinks) stats <- make.link(linktemp) else if (is.character(link)) { stats <- make.link(link) linktemp <- link } else { if (inherits(link, "link-glm")) { stats <- link if (!is.null(stats$name)) linktemp <- stats$name } else { stop(gettextf("link \"%s\" not available for binomial family; available links are %s", linktemp, paste(sQuote(okLinks), collapse = ", ")), domain = NA) } } variance <- function(mu) mu * (1 - mu) validmu <- function(mu) all(is.finite(mu)) && all(mu > 0 & mu < 1) dev.resids <- function(y, mu, wt) .Call(C_binomial_dev_resids, y, mu, wt) aic <- function(y, n, mu, wt, dev) { m <- if (any(n > 1)) n else wt -2 * sum(ifelse(m > 0, (wt/m), 0) * dbinom(round(m * y), round(m), mu, log = TRUE)) } initialize <- expression({ if (NCOL(y) == 1) { if (is.factor(y)) y <- y != levels(y)[1L] n <- rep.int(1, nobs) y[weights == 0] <- 0 if (any(y < 0 | y > 1)) stop("y values must be 0 <= y <= 1") mustart <- (weights * y + 0.5)/(weights + 1) m <- weights * y if (any(abs(m - round(m)) > 0.001)) warning("non-integer #successes in a binomial glm!") } else if (NCOL(y) == 2) { if (any(abs(y - round(y)) > 0.001)) warning("non-integer counts in a binomial glm!") n <- y[, 1] + y[, 2] y <- ifelse(n == 0, 0, y[, 1]/n) weights <- weights * n mustart <- (n * y + 0.5)/(n + 1) } else stop("for the 'binomial' family, y must be a vector of 0 and 1's\nor a 2 column matrix where col 1 is no. successes and col 2 is no. failures") }) simfun <- function(object, nsim) { ftd <- fitted(object) n <- length(ftd) ntot <- n * nsim wts <- object$prior.weights if (any(wts%%1 != 0)) stop("cannot simulate from non-integer prior.weights") if (!is.null(m <- object$model)) { y <- model.response(m) if (is.factor(y)) { yy <- factor(1 + rbinom(ntot, size = 1, prob = ftd), labels = levels(y)) split(yy, rep(seq_len(nsim), each = n)) } else if (is.matrix(y) && ncol(y) == 2) { yy <- vector("list", nsim) for (i in seq_len(nsim)) { Y <- rbinom(n, size = wts, prob = ftd) YY <- cbind(Y, wts - Y) colnames(YY) <- colnames(y) yy[[i]] <- YY } yy } else rbinom(ntot, size = wts, prob = ftd)/wts } else rbinom(ntot, size = wts, prob = ftd)/wts } structure(list(family = "binomial", link = linktemp, linkfun = stats$linkfun, linkinv = stats$linkinv, variance = variance, dev.resids = dev.resids, aic = aic, mu.eta = stats$mu.eta, initialize = initialize, validmu = validmu, valideta = stats$valideta, simulate = simfun), class = "family") }), .Names = "family"))
##
## Out of bag statistics:
##
## Accuracy Kappa
## 0% 0.7781 0.5531
## 2.5% 0.7809 0.5579
## 25% 0.8022 0.6019
## 50% 0.8177 0.6329
## 75% 0.8260 0.6483
## 97.5% 0.8384 0.6763
## 100% 0.8417 0.6786
##
## Model Selection Statistics:
##
## Num Terms Num Variables
## Length:504 Length:504
## Class :character Class :character
## Mode :character Mode :character
## sold.fctr sold.fctr.predict.CSM.X.spatialSign.bagEarth.N
## 1 N 464
## 2 Y 77
## sold.fctr.predict.CSM.X.spatialSign.bagEarth.Y
## 1 84
## 2 393
## Prediction
## Reference N Y
## N 464 84
## Y 77 393
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.418468e-01 6.821625e-01 8.179582e-01 8.637385e-01 5.383104e-01
## AccuracyPValue McnemarPValue
## 1.320668e-93 6.363086e-01
## sold.fctr sold.fctr.predict.CSM.X.spatialSign.bagEarth.N
## 1 N 356
## 2 Y 81
## sold.fctr.predict.CSM.X.spatialSign.bagEarth.Y
## 1 91
## 2 302
## Prediction
## Reference N Y
## N 356 91
## Y 81 302
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.927711e-01 5.838410e-01 7.635769e-01 8.198686e-01 5.385542e-01
## AccuracyPValue McnemarPValue
## 9.989188e-53 4.925595e-01
## id
## 1 CSM.X.spatialSign.bagEarth
## feats
## 1 spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 27.872 13.342
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8411555 0.9014599 0.7808511 0.9262968
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.8299894 0.8149705
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8179582 0.8637385 0.6248342
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.7896595 0.8456376 0.7336815 0.8595102
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.4 0.7783505 0.7927711
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7635769 0.8198686 0.583841 # If (All|RFE).X.glm is less accurate than Low.Cor.X.glm
# check NA coefficients & filter appropriate terms in indep_vars_vctr
# if (method == "glm") {
# orig_glm <- glb_models_lst[[paste0(mdl_id, ".", model_method)]]$finalModel
# orig_glm <- glb_models_lst[["All.X.glm"]]$finalModel; print(summary(orig_glm))
# orig_glm <- glb_models_lst[["RFE.X.glm"]]$finalModel; print(summary(orig_glm))
# require(car)
# vif_orig_glm <- vif(orig_glm); print(vif_orig_glm)
# # if vif errors out with "there are aliased coefficients in the model"
# alias_orig_glm <- alias(orig_glm); alias_complete_orig_glm <- (alias_orig_glm$Complete > 0); alias_complete_orig_glm <- alias_complete_orig_glm[rowSums(alias_complete_orig_glm) > 0, colSums(alias_complete_orig_glm) > 0]; print(alias_complete_orig_glm)
# print(vif_orig_glm[!is.na(vif_orig_glm) & (vif_orig_glm == Inf)])
# print(which.max(vif_orig_glm))
# print(sort(vif_orig_glm[vif_orig_glm >= 1.0e+03], decreasing=TRUE))
# glb_fitobs_df[c(1143, 3637, 3953, 4105), c("UniqueID", "Popular", "H.P.quandary", "Headline")]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE), ]
# all.equal(glb_allobs_df$S.chrs.uppr.n.log, glb_allobs_df$A.chrs.uppr.n.log)
# cor(glb_allobs_df$S.T.herald, glb_allobs_df$S.T.tribun)
# mydsp_obs(Abstract.contains="[Dd]iar", cols=("Abstract"), all=TRUE)
# subset(glb_feats_df, cor.y.abs <= glb_feats_df[glb_feats_df$id == ".rnorm", "cor.y.abs"])
# corxx_mtrx <- cor(data.matrix(glb_allobs_df[, setdiff(names(glb_allobs_df), myfind_chr_cols_df(glb_allobs_df))]), use="pairwise.complete.obs"); abs_corxx_mtrx <- abs(corxx_mtrx); diag(abs_corxx_mtrx) <- 0
# which.max(abs_corxx_mtrx["S.T.tribun", ])
# abs_corxx_mtrx["A.npnct08.log", "S.npnct08.log"]
# step_glm <- step(orig_glm)
# }
# Since caret does not optimize rpart well
# if (method == "rpart")
# ret_lst <- myfit_mdl(mdl_id=paste0(mdl_id_pfx, ".cp.0"), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))
# User specified
# Ensure at least 2 vars in each regression; else varImp crashes
# sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df; sav_featsimp_df <- glb_featsimp_df; all.equal(sav_featsimp_df, glb_featsimp_df)
# glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df; glm_featsimp_df <- sav_featsimp_df
# easier to exclude features
# require(gdata) # needed for trim
# mdl_id <- "";
# indep_vars_vctr <- head(subset(glb_models_df, grepl("All\\.X\\.", mdl_id), select=feats)
# , 1)[, "feats"]
# indep_vars_vctr <- trim(unlist(strsplit(indep_vars_vctr, "[,]")))
# indep_vars_vctr <- setdiff(indep_vars_vctr, ".rnorm")
# easier to include features
#stop(here"); sav_models_df <- glb_models_df; glb_models_df <- sav_models_df
# !_sp
# mdl_id <- "csm"; indep_vars_vctr <- c(NULL
# ,"prdline.my.fctr", "prdline.my.fctr:.clusterid.fctr"
# ,"prdline.my.fctr*biddable"
# #,"prdline.my.fctr*startprice.log"
# #,"prdline.my.fctr*startprice.diff"
# ,"prdline.my.fctr*condition.fctr"
# ,"prdline.my.fctr*D.terms.post.stop.n"
# #,"prdline.my.fctr*D.terms.post.stem.n"
# ,"prdline.my.fctr*cellular.fctr"
# # ,"<feat1>:<feat2>"
# )
# for (method in glbMdlMethods) {
# ret_lst <- myfit_mdl(mdl_id=mdl_id, model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_rcv_n_folds, tune_models_df=glb_tune_models_df)
# csm_mdl_id <- paste0(mdl_id, ".", method)
# csm_featsimp_df <- myget_feats_importance(glb_models_lst[[paste0(mdl_id, ".",
# method)]]); print(head(csm_featsimp_df))
# }
###
# Ntv.1.lm <- lm(reformulate(indep_vars_vctr, glb_rsp_var), glb_trnobs_df); print(summary(Ntv.1.lm))
#glb_models_df[, "max.Accuracy.OOB", FALSE]
#varImp(glb_models_lst[["Low.cor.X.glm"]])
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.2.glm"]])$importance)
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.3.glm"]])$importance)
#glb_feats_df[grepl("npnct28", glb_feats_df$id), ]
# User specified bivariate models
# indep_vars_vctr_lst <- list()
# for (feat in setdiff(names(glb_fitobs_df),
# union(glb_rsp_var, glbFeatsExclude)))
# indep_vars_vctr_lst[["feat"]] <- feat
# User specified combinatorial models
# indep_vars_vctr_lst <- list()
# combn_mtrx <- combn(c("<feat1_name>", "<feat2_name>", "<featn_name>"),
# <num_feats_to_choose>)
# for (combn_ix in 1:ncol(combn_mtrx))
# #print(combn_mtrx[, combn_ix])
# indep_vars_vctr_lst[[combn_ix]] <- combn_mtrx[, combn_ix]
# template for myfit_mdl
# rf is hard-coded in caret to recognize only Accuracy / Kappa evaluation metrics
# only for OOB in trainControl ?
# ret_lst <- myfit_mdl_fn(mdl_id=paste0(mdl_id_pfx, ""), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_rcv_n_folds, tune_models_df=glb_tune_models_df,
# model_loss_mtrx=glbMdlMetric_terms,
# model_summaryFunction=glbMdlMetricSummaryFn,
# model_metric=glbMdlMetricSummary,
# model_metric_maximize=glbMdlMetricMaximize)
# Simplify a model
# fit_df <- glb_fitobs_df; glb_mdl <- step(<complex>_mdl)
# Non-caret models
# rpart_area_mdl <- rpart(reformulate("Area", response=glb_rsp_var),
# data=glb_fitobs_df, #method="class",
# control=rpart.control(cp=0.12),
# parms=list(loss=glbMdlMetric_terms))
# print("rpart_sel_wlm_mdl"); prp(rpart_sel_wlm_mdl)
#
print(glb_models_df)## id
## MFO.myMFO_classfr MFO.myMFO_classfr
## Random.myrandom_classfr Random.myrandom_classfr
## Max.cor.Y.rcv.1X1.glmnet Max.cor.Y.rcv.1X1.glmnet
## Max.cor.Y.rcv.3X1.glmnet Max.cor.Y.rcv.3X1.glmnet
## Max.cor.Y.rcv.3X3.glmnet Max.cor.Y.rcv.3X3.glmnet
## Max.cor.Y.rcv.3X5.glmnet Max.cor.Y.rcv.3X5.glmnet
## Max.cor.Y.rcv.5X1.glmnet Max.cor.Y.rcv.5X1.glmnet
## Max.cor.Y.rcv.5X3.glmnet Max.cor.Y.rcv.5X3.glmnet
## Max.cor.Y.rcv.5X5.glmnet Max.cor.Y.rcv.5X5.glmnet
## Max.cor.Y.rcv.1X1.cp.0.rpart Max.cor.Y.rcv.1X1.cp.0.rpart
## Max.cor.Y.rpart Max.cor.Y.rpart
## Interact.High.cor.Y.glmnet Interact.High.cor.Y.glmnet
## Low.cor.X.glmnet Low.cor.X.glmnet
## CSM.X.bagEarth CSM.X.bagEarth
## CSM.X.glm CSM.X.glm
## CSM.X.bayesglm CSM.X.bayesglm
## CSM.X.glmnet CSM.X.glmnet
## CSM.X.nnet CSM.X.nnet
## CSM.X.rpart CSM.X.rpart
## CSM.X.gbm CSM.X.gbm
## CSM.X.avNNet CSM.X.avNNet
## CSM.X.rf CSM.X.rf
## CSM.X.earth CSM.X.earth
## RFE.X.glmnet RFE.X.glmnet
## RFE.X.glm RFE.X.glm
## All.X.glmnet All.X.glmnet
## CSM.X.Interact.glmnet CSM.X.Interact.glmnet
## CSM.X.YeoJohnson.bagEarth CSM.X.YeoJohnson.bagEarth
## CSM.X.center.scale.bagEarth CSM.X.center.scale.bagEarth
## CSM.X.spatialSign.bagEarth CSM.X.spatialSign.bagEarth
## feats
## MFO.myMFO_classfr .rnorm
## Random.myrandom_classfr .rnorm
## Max.cor.Y.rcv.1X1.glmnet biddable,sprice.root2
## Max.cor.Y.rcv.3X1.glmnet biddable,sprice.root2
## Max.cor.Y.rcv.3X3.glmnet biddable,sprice.root2
## Max.cor.Y.rcv.3X5.glmnet biddable,sprice.root2
## Max.cor.Y.rcv.5X1.glmnet biddable,sprice.root2
## Max.cor.Y.rcv.5X3.glmnet biddable,sprice.root2
## Max.cor.Y.rcv.5X5.glmnet biddable,sprice.root2
## Max.cor.Y.rcv.1X1.cp.0.rpart biddable,sprice.root2
## Max.cor.Y.rpart biddable,sprice.root2
## Interact.High.cor.Y.glmnet biddable,sprice.root2,biddable:sprice.log10,biddable:D.terms.post.stop.n.log,biddable:startprice.dcm2.is9,biddable:D.weight.post.stem.sum,biddable:D.chrs.n.log,biddable:cellular.fctr,biddable:D.terms.post.stem.n.log,biddable:sprice.root2
## Low.cor.X.glmnet biddable,spdiff.cut.fctr,startprice.dcm2.is9,D.wrds.stop.n.log,D.weight.sum.stem.stop.Ratio,D.ratio.weight.sum.wrds.n,.rnorm,storage.fctr,D.chrs.pnct11.n.log,startprice.dgt2.is9,color.fctr,prdl.my.fctr,D.terms.post.stop.n.log,cellular.fctr,startprice.dgt1.is9,condition.fctr,sprice.root2,prdl.my.fctr:.clusterid.fctr
## CSM.X.bagEarth spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## CSM.X.glm spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## CSM.X.bayesglm spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## CSM.X.glmnet spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## CSM.X.nnet spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## CSM.X.rpart spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## CSM.X.gbm spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## CSM.X.avNNet spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## CSM.X.rf spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## CSM.X.earth spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## RFE.X.glmnet sprice.d20nexp,sprice.log10,sprice.root2,biddable,spdiff.cut.fctr,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## RFE.X.glm sprice.d20nexp,sprice.log10,sprice.root2,biddable,spdiff.cut.fctr,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## All.X.glmnet biddable,sprice.d20nexp,spdiff.cut.fctr,D.ratio.wrds.stop.n.wrds.n,startprice.dcm2.is9,D.wrds.stop.n.log,D.weight.sum.stem.stop.Ratio,D.ratio.weight.sum.wrds.n,.rnorm,startprice.dcm1.is9,storage.fctr,D.chrs.pnct11.n.log,D.chrs.pnct13.n.log,startprice.dgt2.is9,color.fctr,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,prdl.my.fctr,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.n.log,D.terms.post.stem.n.log,D.wrds.unq.n.log,D.terms.post.stop.n.log,cellular.fctr,startprice.dgt1.is9,condition.fctr,sprice.log10,sprice.root2,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## CSM.X.Interact.glmnet .rnorm,D.weight.sum,D.weight.post.stem.sum,D.chrs.pnct13.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,startprice.dcm2.is9,sprice.root2,sprice.log10,startprice.dcm1.is9,D.weight.sum.stem.stop.Ratio,spdiff.cut.fctr*biddable,spdiff.cut.fctr*storage.fctr,spdiff.cut.fctr*prdl.my.fctr,spdiff.cut.fctr*cellular.fctr,spdiff.cut.fctr*D.chrs.pnct11.n.log,spdiff.cut.fctr*condition.fctr,spdiff.cut.fctr*color.fctr,spdiff.cut.fctr*sprice.d20nexp,spdiff.cut.fctr*startprice.dgt2.is9,spdiff.cut.fctr*D.wrds.n.log,spdiff.cut.fctr*D.chrs.n.log,spdiff.cut.fctr*D.chrs.uppr.n.log,spdiff.cut.fctr*D.terms.post.stem.n.log,spdiff.cut.fctr*D.terms.post.stop.n.log,spdiff.cut.fctr*D.wrds.stop.n.log,spdiff.cut.fctr*D.wrds.unq.n.log,spdiff.cut.fctr*startprice.dgt1.is9,spdiff.cut.fctr*D.weight.post.stop.sum,spdiff.cut.fctr*cellular.fctr:carrier.fctr,spdiff.cut.fctr*prdl.my.fctr:.clusterid.fctr
## CSM.X.YeoJohnson.bagEarth spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## CSM.X.center.scale.bagEarth spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## CSM.X.spatialSign.bagEarth spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything
## MFO.myMFO_classfr 0 0.289
## Random.myrandom_classfr 0 0.291
## Max.cor.Y.rcv.1X1.glmnet 0 0.711
## Max.cor.Y.rcv.3X1.glmnet 25 1.470
## Max.cor.Y.rcv.3X3.glmnet 25 2.053
## Max.cor.Y.rcv.3X5.glmnet 25 2.454
## Max.cor.Y.rcv.5X1.glmnet 25 1.669
## Max.cor.Y.rcv.5X3.glmnet 25 2.435
## Max.cor.Y.rcv.5X5.glmnet 25 3.132
## Max.cor.Y.rcv.1X1.cp.0.rpart 0 0.772
## Max.cor.Y.rpart 5 1.655
## Interact.High.cor.Y.glmnet 25 2.693
## Low.cor.X.glmnet 25 3.008
## CSM.X.bagEarth 1 119.149
## CSM.X.glm 1 3.088
## CSM.X.bayesglm 1 5.132
## CSM.X.glmnet 25 14.824
## CSM.X.nnet 25 37.288
## CSM.X.rpart 5 3.431
## CSM.X.gbm 25 12.319
## CSM.X.avNNet 25 116.672
## CSM.X.rf 5 14.230
## CSM.X.earth 5 8.246
## RFE.X.glmnet 25 15.649
## RFE.X.glm 1 2.905
## All.X.glmnet 25 14.564
## CSM.X.Interact.glmnet 25 39.004
## CSM.X.YeoJohnson.bagEarth 1 116.682
## CSM.X.center.scale.bagEarth 1 124.921
## CSM.X.spatialSign.bagEarth 1 27.872
## min.elapsedtime.final max.AUCpROC.fit
## MFO.myMFO_classfr 0.002 0.5000000
## Random.myrandom_classfr 0.002 0.4859659
## Max.cor.Y.rcv.1X1.glmnet 0.022 0.7898522
## Max.cor.Y.rcv.3X1.glmnet 0.017 0.7919708
## Max.cor.Y.rcv.3X3.glmnet 0.016 0.7919708
## Max.cor.Y.rcv.3X5.glmnet 0.015 0.7919708
## Max.cor.Y.rcv.5X1.glmnet 0.016 0.7956204
## Max.cor.Y.rcv.5X3.glmnet 0.015 0.7956204
## Max.cor.Y.rcv.5X5.glmnet 0.019 0.7963859
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.015 0.8281424
## Max.cor.Y.rpart 0.016 0.8102808
## Interact.High.cor.Y.glmnet 0.096 0.7970973
## Low.cor.X.glmnet 0.116 0.8597751
## CSM.X.bagEarth 58.531 0.8913146
## CSM.X.glm 0.233 0.8654721
## CSM.X.bayesglm 0.401 0.8590891
## CSM.X.glmnet 0.552 0.8545310
## CSM.X.nnet 0.216 0.8654721
## CSM.X.rpart 0.081 0.8142763
## CSM.X.gbm 1.007 0.8910157
## CSM.X.avNNet 0.308 0.8952788
## CSM.X.rf 5.938 0.9990876
## CSM.X.earth 0.937 0.8328001
## RFE.X.glmnet 1.610 0.8606455
## RFE.X.glm 0.190 0.8595840
## All.X.glmnet 0.515 0.8626593
## CSM.X.Interact.glmnet 2.421 0.8557466
## CSM.X.YeoJohnson.bagEarth 57.979 0.8834097
## CSM.X.center.scale.bagEarth 62.555 0.8862983
## CSM.X.spatialSign.bagEarth 13.342 0.8411555
## max.Sens.fit max.Spec.fit max.AUCROCR.fit
## MFO.myMFO_classfr 1.0000000 0.0000000 0.5000000
## Random.myrandom_classfr 0.5164234 0.4555085 0.4893217
## Max.cor.Y.rcv.1X1.glmnet 0.8339416 0.7457627 0.8620600
## Max.cor.Y.rcv.3X1.glmnet 0.8339416 0.7500000 0.8621335
## Max.cor.Y.rcv.3X3.glmnet 0.8339416 0.7500000 0.8622147
## Max.cor.Y.rcv.3X5.glmnet 0.8339416 0.7500000 0.8622069
## Max.cor.Y.rcv.5X1.glmnet 0.8412409 0.7500000 0.8621064
## Max.cor.Y.rcv.5X3.glmnet 0.8412409 0.7500000 0.8621064
## Max.cor.Y.rcv.5X5.glmnet 0.8448905 0.7478814 0.8621064
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.8978102 0.7584746 0.8816304
## Max.cor.Y.rpart 0.8959854 0.7245763 0.8387008
## Interact.High.cor.Y.glmnet 0.8886861 0.7055085 0.8639370
## Low.cor.X.glmnet 0.8996350 0.8199153 0.9329264
## CSM.X.bagEarth 0.9251825 0.8574468 0.9584990
## CSM.X.glm 0.9032847 0.8276596 0.9415592
## CSM.X.bayesglm 0.9032847 0.8148936 0.9370632
## CSM.X.glmnet 0.9069343 0.8021277 0.9345318
## CSM.X.nnet 0.9032847 0.8276596 0.9395830
## CSM.X.rpart 0.9689781 0.6595745 0.8400159
## CSM.X.gbm 0.9416058 0.8404255 0.9666078
## CSM.X.avNNet 0.9671533 0.8234043 0.9315849
## CSM.X.rf 0.9981752 1.0000000 0.9999981
## CSM.X.earth 0.9124088 0.7531915 0.9006872
## RFE.X.glmnet 0.8996350 0.8216561 0.9339695
## RFE.X.glm 0.8996350 0.8195329 0.9391534
## All.X.glmnet 0.9032847 0.8220339 0.9355785
## CSM.X.Interact.glmnet 0.9233577 0.7881356 0.9302201
## CSM.X.YeoJohnson.bagEarth 0.9178832 0.8489362 0.9574934
## CSM.X.center.scale.bagEarth 0.9215328 0.8510638 0.9583825
## CSM.X.spatialSign.bagEarth 0.9014599 0.7808511 0.9262968
## opt.prob.threshold.fit max.f.score.fit
## MFO.myMFO_classfr 0.4 0.6327078
## Random.myrandom_classfr 0.4 0.6327078
## Max.cor.Y.rcv.1X1.glmnet 0.4 0.7698745
## Max.cor.Y.rcv.3X1.glmnet 0.5 0.7720829
## Max.cor.Y.rcv.3X3.glmnet 0.5 0.7720829
## Max.cor.Y.rcv.3X5.glmnet 0.5 0.7720829
## Max.cor.Y.rcv.5X1.glmnet 0.5 0.7754655
## Max.cor.Y.rcv.5X3.glmnet 0.5 0.7754655
## Max.cor.Y.rcv.5X5.glmnet 0.5 0.7758242
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.4 0.8102109
## Max.cor.Y.rpart 0.5 0.7853042
## Interact.High.cor.Y.glmnet 0.4 0.7724750
## Low.cor.X.glmnet 0.5 0.8468271
## CSM.X.bagEarth 0.5 0.8818381
## CSM.X.glm 0.4 0.8550420
## CSM.X.bayesglm 0.4 0.8502618
## CSM.X.glmnet 0.4 0.8484211
## CSM.X.nnet 0.4 0.8538622
## CSM.X.rpart 0.9 0.7779172
## CSM.X.gbm 0.4 0.9100418
## CSM.X.avNNet 0.4 0.8878924
## CSM.X.rf 0.5 0.9989373
## CSM.X.earth 0.4 0.8221757
## RFE.X.glmnet 0.4 0.8482328
## RFE.X.glm 0.4 0.8493724
## All.X.glmnet 0.4 0.8503119
## CSM.X.Interact.glmnet 0.4 0.8601036
## CSM.X.YeoJohnson.bagEarth 0.5 0.8730853
## CSM.X.center.scale.bagEarth 0.5 0.8762322
## CSM.X.spatialSign.bagEarth 0.4 0.8299894
## max.Accuracy.fit max.AccuracyLower.fit
## MFO.myMFO_classfr 0.4627451 0.4318011
## Random.myrandom_classfr 0.4627451 0.4318011
## Max.cor.Y.rcv.1X1.glmnet 0.7843137 0.7577802
## Max.cor.Y.rcv.3X1.glmnet 0.7950911 0.7690020
## Max.cor.Y.rcv.3X3.glmnet 0.7954221 0.7690020
## Max.cor.Y.rcv.3X5.glmnet 0.7962655 0.7690020
## Max.cor.Y.rcv.5X1.glmnet 0.7990899 0.7730893
## Max.cor.Y.rcv.5X3.glmnet 0.7964364 0.7730893
## Max.cor.Y.rcv.5X5.glmnet 0.7970687 0.7741117
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.8323529 0.8079872
## Max.cor.Y.rpart 0.7964073 0.7915283
## Interact.High.cor.Y.glmnet 0.8026098 0.7730893
## Low.cor.X.glmnet 0.8349657 0.8400893
## CSM.X.bagEarth 0.8024729 0.8733491
## CSM.X.glm 0.8264355 0.8418654
## CSM.X.bayesglm 0.8293912 0.8366530
## CSM.X.glmnet 0.8323381 0.8356115
## CSM.X.nnet 0.8267681 0.8397793
## CSM.X.rpart 0.8251457 0.8014236
## CSM.X.gbm 0.8323391 0.8967192
## CSM.X.avNNet 0.8271026 0.8818153
## CSM.X.rf 0.8310413 0.9945391
## CSM.X.earth 0.8241614 0.8086485
## RFE.X.glmnet 0.8259789 0.8336909
## RFE.X.glm 0.8217402 0.8357707
## All.X.glmnet 0.8303886 0.8359295
## CSM.X.Interact.glmnet 0.8316958 0.8452972
## CSM.X.YeoJohnson.bagEarth 0.8010417 0.8649151
## CSM.X.center.scale.bagEarth 0.7965221 0.8680742
## CSM.X.spatialSign.bagEarth 0.8149705 0.8179582
## max.AccuracyUpper.fit max.Kappa.fit
## MFO.myMFO_classfr 0.4939047 0.0000000
## Random.myrandom_classfr 0.4939047 0.0000000
## Max.cor.Y.rcv.1X1.glmnet 0.8091950 0.5669826
## Max.cor.Y.rcv.3X1.glmnet 0.8194783 0.5860164
## Max.cor.Y.rcv.3X3.glmnet 0.8194783 0.5866808
## Max.cor.Y.rcv.3X5.glmnet 0.8194783 0.5883400
## Max.cor.Y.rcv.5X1.glmnet 0.8232111 0.5944245
## Max.cor.Y.rcv.5X3.glmnet 0.8232111 0.5888026
## Max.cor.Y.rcv.5X5.glmnet 0.8241437 0.5901123
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.8547824 0.6606905
## Max.cor.Y.rpart 0.8399619 0.5852116
## Interact.High.cor.Y.glmnet 0.8232111 0.5988972
## Low.cor.X.glmnet 0.8832827 0.6666179
## CSM.X.bagEarth 0.9121576 0.6018620
## CSM.X.glm 0.8848822 0.6495141
## CSM.X.bayesglm 0.8803011 0.6549643
## CSM.X.glmnet 0.8793838 0.6607067
## CSM.X.nnet 0.8830508 0.6499515
## CSM.X.rpart 0.8489327 0.6414684
## CSM.X.gbm 0.9318742 0.6607657
## CSM.X.avNNet 0.9193597 0.6493725
## CSM.X.rf 0.9999751 0.6583279
## CSM.X.earth 0.8554193 0.6443967
## RFE.X.glmnet 0.8776697 0.6488269
## RFE.X.glm 0.8795035 0.6404864
## All.X.glmnet 0.8796230 0.6576319
## CSM.X.Interact.glmnet 0.8878492 0.6584576
## CSM.X.YeoJohnson.bagEarth 0.9049229 0.5969627
## CSM.X.center.scale.bagEarth 0.9076395 0.5900610
## CSM.X.spatialSign.bagEarth 0.8637385 0.6248342
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB
## MFO.myMFO_classfr 0.5000000 1.0000000 0.0000000
## Random.myrandom_classfr 0.5064252 0.5480984 0.4647520
## Max.cor.Y.rcv.1X1.glmnet 0.7665376 0.8255034 0.7075718
## Max.cor.Y.rcv.3X1.glmnet 0.7592654 0.8187919 0.6997389
## Max.cor.Y.rcv.3X3.glmnet 0.7603840 0.8210291 0.6997389
## Max.cor.Y.rcv.3X5.glmnet 0.7603840 0.8210291 0.6997389
## Max.cor.Y.rcv.5X1.glmnet 0.7615026 0.8232662 0.6997389
## Max.cor.Y.rcv.5X3.glmnet 0.7615026 0.8232662 0.6997389
## Max.cor.Y.rcv.5X5.glmnet 0.7641135 0.8232662 0.7049608
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.7504950 0.8456376 0.6553525
## Max.cor.Y.rpart 0.7445254 0.8545861 0.6344648
## Interact.High.cor.Y.glmnet 0.7551533 0.8680089 0.6422977
## Low.cor.X.glmnet 0.7801532 0.8187919 0.7415144
## CSM.X.bagEarth 0.8036460 0.8501119 0.7571802
## CSM.X.glm 0.7836958 0.8232662 0.7441253
## CSM.X.bayesglm 0.7902203 0.8389262 0.7415144
## CSM.X.glmnet 0.7937629 0.8434004 0.7441253
## CSM.X.nnet 0.7883570 0.8299776 0.7467363
## CSM.X.rpart 0.7534652 0.9194631 0.5874674
## CSM.X.gbm 0.7573963 0.8411633 0.6736292
## CSM.X.avNNet 0.7797676 0.8859060 0.6736292
## CSM.X.rf 0.7831321 0.8456376 0.7206266
## CSM.X.earth 0.8002815 0.8903803 0.7101828
## RFE.X.glmnet 0.7861198 0.8255034 0.7467363
## RFE.X.glm 0.7851882 0.8210291 0.7493473
## All.X.glmnet 0.7835089 0.8255034 0.7415144
## CSM.X.Interact.glmnet 0.7820106 0.8590604 0.7049608
## CSM.X.YeoJohnson.bagEarth 0.8060701 0.8523490 0.7597911
## CSM.X.center.scale.bagEarth 0.8070017 0.8568233 0.7571802
## CSM.X.spatialSign.bagEarth 0.7896595 0.8456376 0.7336815
## max.AUCROCR.OOB opt.prob.threshold.OOB
## MFO.myMFO_classfr 0.5000000 0.4
## Random.myrandom_classfr 0.4956046 0.4
## Max.cor.Y.rcv.1X1.glmnet 0.8197236 0.4
## Max.cor.Y.rcv.3X1.glmnet 0.8197353 0.4
## Max.cor.Y.rcv.3X3.glmnet 0.8197470 0.4
## Max.cor.Y.rcv.3X5.glmnet 0.8196593 0.4
## Max.cor.Y.rcv.5X1.glmnet 0.8195600 0.4
## Max.cor.Y.rcv.5X3.glmnet 0.8195600 0.4
## Max.cor.Y.rcv.5X5.glmnet 0.8193790 0.4
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.7934621 0.3
## Max.cor.Y.rpart 0.7855854 0.3
## Interact.High.cor.Y.glmnet 0.8189000 0.3
## Low.cor.X.glmnet 0.8727052 0.3
## CSM.X.bagEarth 0.8766246 0.5
## CSM.X.glm 0.8705557 0.5
## CSM.X.bayesglm 0.8720510 0.3
## CSM.X.glmnet 0.8713734 0.4
## CSM.X.nnet 0.8693232 0.5
## CSM.X.rpart 0.7890637 0.3
## CSM.X.gbm 0.8482836 0.3
## CSM.X.avNNet 0.8614202 0.3
## CSM.X.rf 0.8492707 0.3
## CSM.X.earth 0.8533245 0.4
## RFE.X.glmnet 0.8728278 0.3
## RFE.X.glm 0.8700066 0.5
## All.X.glmnet 0.8721444 0.3
## CSM.X.Interact.glmnet 0.8636807 0.4
## CSM.X.YeoJohnson.bagEarth 0.8782951 0.4
## CSM.X.center.scale.bagEarth 0.8764143 0.5
## CSM.X.spatialSign.bagEarth 0.8595102 0.4
## max.f.score.OOB max.Accuracy.OOB
## MFO.myMFO_classfr 0.6314922 0.4614458
## Random.myrandom_classfr 0.6314922 0.4614458
## Max.cor.Y.rcv.1X1.glmnet 0.7535854 0.7722892
## Max.cor.Y.rcv.3X1.glmnet 0.7519182 0.7662651
## Max.cor.Y.rcv.3X3.glmnet 0.7525510 0.7662651
## Max.cor.Y.rcv.3X5.glmnet 0.7515924 0.7650602
## Max.cor.Y.rcv.5X1.glmnet 0.7506361 0.7638554
## Max.cor.Y.rcv.5X3.glmnet 0.7506361 0.7638554
## Max.cor.Y.rcv.5X5.glmnet 0.7512690 0.7638554
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.7313997 0.7433735
## Max.cor.Y.rpart 0.7394270 0.7698795
## Interact.High.cor.Y.glmnet 0.7522698 0.7698795
## Low.cor.X.glmnet 0.7719715 0.7686747
## CSM.X.bagEarth 0.7837838 0.8072289
## CSM.X.glm 0.7630522 0.7867470
## CSM.X.bayesglm 0.7705113 0.7674699
## CSM.X.glmnet 0.7735369 0.7855422
## CSM.X.nnet 0.7677852 0.7915663
## CSM.X.rpart 0.7340426 0.7590361
## CSM.X.gbm 0.7684848 0.7698795
## CSM.X.avNNet 0.7644684 0.7891566
## CSM.X.rf 0.7605295 0.7602410
## CSM.X.earth 0.7878018 0.7987952
## RFE.X.glmnet 0.7668639 0.7626506
## RFE.X.glm 0.7653333 0.7879518
## All.X.glmnet 0.7654028 0.7614458
## CSM.X.Interact.glmnet 0.7898089 0.8012048
## CSM.X.YeoJohnson.bagEarth 0.7888041 0.8000000
## CSM.X.center.scale.bagEarth 0.7869742 0.8108434
## CSM.X.spatialSign.bagEarth 0.7783505 0.7927711
## max.AccuracyLower.OOB max.AccuracyUpper.OOB
## MFO.myMFO_classfr 0.4271177 0.4960486
## Random.myrandom_classfr 0.4271177 0.4960486
## Max.cor.Y.rcv.1X1.glmnet 0.7422177 0.8004125
## Max.cor.Y.rcv.3X1.glmnet 0.7359543 0.7946712
## Max.cor.Y.rcv.3X3.glmnet 0.7359543 0.7946712
## Max.cor.Y.rcv.3X5.glmnet 0.7347026 0.7935220
## Max.cor.Y.rcv.5X1.glmnet 0.7334512 0.7923725
## Max.cor.Y.rcv.5X3.glmnet 0.7334512 0.7923725
## Max.cor.Y.rcv.5X5.glmnet 0.7334512 0.7923725
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.7122254 0.7727821
## Max.cor.Y.rpart 0.7397113 0.7981170
## Interact.High.cor.Y.glmnet 0.7397113 0.7981170
## Low.cor.X.glmnet 0.7384586 0.7969687
## CSM.X.bagEarth 0.7787174 0.8335386
## CSM.X.glm 0.7572842 0.8141569
## CSM.X.bayesglm 0.7372063 0.7958201
## CSM.X.glmnet 0.7560267 0.8130134
## CSM.X.nnet 0.7623176 0.8187270
## CSM.X.rpart 0.7284488 0.7877710
## CSM.X.gbm 0.7397113 0.7981170
## CSM.X.avNNet 0.7598002 0.8164427
## CSM.X.rf 0.7296990 0.7889219
## CSM.X.earth 0.7698788 0.8255712
## RFE.X.glmnet 0.7322001 0.7912226
## RFE.X.glm 0.7585420 0.8152999
## All.X.glmnet 0.7309494 0.7900724
## CSM.X.Interact.glmnet 0.7724022 0.8278495
## CSM.X.YeoJohnson.bagEarth 0.7711403 0.8267105
## CSM.X.center.scale.bagEarth 0.7825114 0.8369472
## CSM.X.spatialSign.bagEarth 0.7635769 0.8198686
## max.Kappa.OOB max.AccuracySD.fit
## MFO.myMFO_classfr 0.0000000 NA
## Random.myrandom_classfr 0.0000000 NA
## Max.cor.Y.rcv.1X1.glmnet 0.5419399 NA
## Max.cor.Y.rcv.3X1.glmnet 0.5311363 0.003934893
## Max.cor.Y.rcv.3X3.glmnet 0.5313109 0.020088705
## Max.cor.Y.rcv.3X5.glmnet 0.5289828 0.018120665
## Max.cor.Y.rcv.5X1.glmnet 0.5266555 0.036086436
## Max.cor.Y.rcv.5X3.glmnet 0.5266555 0.033491865
## Max.cor.Y.rcv.5X5.glmnet 0.5268317 0.031290870
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.4862697 NA
## Max.cor.Y.rpart 0.5341327 0.020068991
## Interact.High.cor.Y.glmnet 0.5374385 0.017969767
## Low.cor.X.glmnet 0.5411011 0.009378705
## CSM.X.bagEarth 0.6102577 NA
## CSM.X.glm 0.5694138 0.014014509
## CSM.X.bayesglm 0.5386259 0.009290129
## CSM.X.glmnet 0.5701259 0.013081889
## CSM.X.nnet 0.5789866 0.012043833
## CSM.X.rpart 0.5139178 0.011980640
## CSM.X.gbm 0.5420564 0.017270583
## CSM.X.avNNet 0.5739595 0.013960421
## CSM.X.rf 0.5234042 NA
## CSM.X.earth 0.5967662 0.012108600
## RFE.X.glmnet 0.5294107 0.016618124
## RFE.X.glm 0.5720872 0.027942867
## All.X.glmnet 0.5269348 0.011266493
## CSM.X.Interact.glmnet 0.6014470 0.015586760
## CSM.X.YeoJohnson.bagEarth 0.5991062 NA
## CSM.X.center.scale.bagEarth 0.6173498 NA
## CSM.X.spatialSign.bagEarth 0.5838410 NA
## max.KappaSD.fit
## MFO.myMFO_classfr NA
## Random.myrandom_classfr NA
## Max.cor.Y.rcv.1X1.glmnet NA
## Max.cor.Y.rcv.3X1.glmnet 0.009324777
## Max.cor.Y.rcv.3X3.glmnet 0.040878031
## Max.cor.Y.rcv.3X5.glmnet 0.036897112
## Max.cor.Y.rcv.5X1.glmnet 0.070687264
## Max.cor.Y.rcv.5X3.glmnet 0.067292776
## Max.cor.Y.rcv.5X5.glmnet 0.062857472
## Max.cor.Y.rcv.1X1.cp.0.rpart NA
## Max.cor.Y.rpart 0.040059154
## Interact.High.cor.Y.glmnet 0.037056533
## Low.cor.X.glmnet 0.019384018
## CSM.X.bagEarth NA
## CSM.X.glm 0.028498041
## CSM.X.bayesglm 0.019197546
## CSM.X.glmnet 0.026691421
## CSM.X.nnet 0.024508200
## CSM.X.rpart 0.024454796
## CSM.X.gbm 0.035051533
## CSM.X.avNNet 0.027968808
## CSM.X.rf NA
## CSM.X.earth 0.023852110
## RFE.X.glmnet 0.033712966
## RFE.X.glm 0.056377715
## All.X.glmnet 0.022766184
## CSM.X.Interact.glmnet 0.032394299
## CSM.X.YeoJohnson.bagEarth NA
## CSM.X.center.scale.bagEarth NA
## CSM.X.spatialSign.bagEarth NArm(ret_lst)
fit.models_1_chunk_df <-
myadd_chunk(fit.models_1_chunk_df, "fit.models_1_end", major.inc = FALSE,
label.minor = "teardown")## label step_major step_minor label_minor bgn
## 20 fit.models_1_preProc 1 19 preProc 877.175
## 21 fit.models_1_end 1 20 teardown 1302.239
## end elapsed
## 20 1302.238 425.063
## 21 NA NAglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)## label step_major step_minor label_minor bgn end elapsed
## 11 fit.models 6 1 1 377.259 1302.249 924.99
## 12 fit.models 6 2 2 1302.250 NA NAfit.models_2_chunk_df <-
myadd_chunk(NULL, "fit.models_2_bgn", label.minor = "setup")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_2_bgn 1 0 setup 1340.9 NA NA#stop(here"); glb_to_sav(); all.equal(glb_models_df, sav_models_df)
# if (!is.null(glbMdlMetricSummaryFn)) {
# stats_df <- glb_models_df[, "id", FALSE]
#
# stats_mdl_df <- data.frame()
# for (mdl_id in stats_df$id) {
# stats_mdl_df <- rbind(stats_mdl_df,
# mypredict_mdl(glb_models_lst[[mdl_id]], glb_fitobs_df, glb_rsp_var,
# glb_rsp_var_out, mdl_id, "fit",
# glbMdlMetricSummaryFn, glbMdlMetricSummary,
# glbMdlMetricMaximize, ret_type="stats"))
# }
# stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
#
# stats_mdl_df <- data.frame()
# for (mdl_id in stats_df$id) {
# stats_mdl_df <- rbind(stats_mdl_df,
# mypredict_mdl(glb_models_lst[[mdl_id]], glb_OOBobs_df, glb_rsp_var,
# glb_rsp_var_out, mdl_id, "OOB",
# glbMdlMetricSummaryFn, glbMdlMetricSummary,
# glbMdlMetricMaximize, ret_type="stats"))
# }
# stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
#
# print("Merging following data into glb_models_df:")
# print(stats_mrg_df <- stats_df[, c(1, grep(glbMdlMetricSummary, names(stats_df)))])
# print(tmp_models_df <- orderBy(~id, glb_models_df[, c("id",
# grep(glbMdlMetricSummary, names(stats_df), value=TRUE))]))
#
# tmp2_models_df <- glb_models_df[, c("id", setdiff(names(glb_models_df),
# grep(glbMdlMetricSummary, names(stats_df), value=TRUE)))]
# tmp3_models_df <- merge(tmp2_models_df, stats_mrg_df, all.x=TRUE, sort=FALSE)
# print(tmp3_models_df)
# print(names(tmp3_models_df))
# print(glb_models_df <- subset(tmp3_models_df, select=-id.1))
# }
plt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
plt_models_df[, sub("min.", "inv.", var)] <-
#ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
1.0 / plt_models_df[, var]
plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
print(plt_models_df)## id
## MFO.myMFO_classfr MFO.myMFO_classfr
## Random.myrandom_classfr Random.myrandom_classfr
## Max.cor.Y.rcv.1X1.glmnet Max.cor.Y.rcv.1X1.glmnet
## Max.cor.Y.rcv.3X1.glmnet Max.cor.Y.rcv.3X1.glmnet
## Max.cor.Y.rcv.3X3.glmnet Max.cor.Y.rcv.3X3.glmnet
## Max.cor.Y.rcv.3X5.glmnet Max.cor.Y.rcv.3X5.glmnet
## Max.cor.Y.rcv.5X1.glmnet Max.cor.Y.rcv.5X1.glmnet
## Max.cor.Y.rcv.5X3.glmnet Max.cor.Y.rcv.5X3.glmnet
## Max.cor.Y.rcv.5X5.glmnet Max.cor.Y.rcv.5X5.glmnet
## Max.cor.Y.rcv.1X1.cp.0.rpart Max.cor.Y.rcv.1X1.cp.0.rpart
## Max.cor.Y.rpart Max.cor.Y.rpart
## Interact.High.cor.Y.glmnet Interact.High.cor.Y.glmnet
## Low.cor.X.glmnet Low.cor.X.glmnet
## CSM.X.bagEarth CSM.X.bagEarth
## CSM.X.glm CSM.X.glm
## CSM.X.bayesglm CSM.X.bayesglm
## CSM.X.glmnet CSM.X.glmnet
## CSM.X.nnet CSM.X.nnet
## CSM.X.rpart CSM.X.rpart
## CSM.X.gbm CSM.X.gbm
## CSM.X.avNNet CSM.X.avNNet
## CSM.X.rf CSM.X.rf
## CSM.X.earth CSM.X.earth
## RFE.X.glmnet RFE.X.glmnet
## RFE.X.glm RFE.X.glm
## All.X.glmnet All.X.glmnet
## CSM.X.Interact.glmnet CSM.X.Interact.glmnet
## CSM.X.YeoJohnson.bagEarth CSM.X.YeoJohnson.bagEarth
## CSM.X.center.scale.bagEarth CSM.X.center.scale.bagEarth
## CSM.X.spatialSign.bagEarth CSM.X.spatialSign.bagEarth
## feats
## MFO.myMFO_classfr .rnorm
## Random.myrandom_classfr .rnorm
## Max.cor.Y.rcv.1X1.glmnet biddable,sprice.root2
## Max.cor.Y.rcv.3X1.glmnet biddable,sprice.root2
## Max.cor.Y.rcv.3X3.glmnet biddable,sprice.root2
## Max.cor.Y.rcv.3X5.glmnet biddable,sprice.root2
## Max.cor.Y.rcv.5X1.glmnet biddable,sprice.root2
## Max.cor.Y.rcv.5X3.glmnet biddable,sprice.root2
## Max.cor.Y.rcv.5X5.glmnet biddable,sprice.root2
## Max.cor.Y.rcv.1X1.cp.0.rpart biddable,sprice.root2
## Max.cor.Y.rpart biddable,sprice.root2
## Interact.High.cor.Y.glmnet biddable,sprice.root2,biddable:sprice.log10,biddable:D.terms.post.stop.n.log,biddable:startprice.dcm2.is9,biddable:D.weight.post.stem.sum,biddable:D.chrs.n.log,biddable:cellular.fctr,biddable:D.terms.post.stem.n.log,biddable:sprice.root2
## Low.cor.X.glmnet biddable,spdiff.cut.fctr,startprice.dcm2.is9,D.wrds.stop.n.log,D.weight.sum.stem.stop.Ratio,D.ratio.weight.sum.wrds.n,.rnorm,storage.fctr,D.chrs.pnct11.n.log,startprice.dgt2.is9,color.fctr,prdl.my.fctr,D.terms.post.stop.n.log,cellular.fctr,startprice.dgt1.is9,condition.fctr,sprice.root2,prdl.my.fctr:.clusterid.fctr
## CSM.X.bagEarth spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## CSM.X.glm spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## CSM.X.bayesglm spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## CSM.X.glmnet spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## CSM.X.nnet spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## CSM.X.rpart spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## CSM.X.gbm spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## CSM.X.avNNet spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## CSM.X.rf spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## CSM.X.earth spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## RFE.X.glmnet sprice.d20nexp,sprice.log10,sprice.root2,biddable,spdiff.cut.fctr,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## RFE.X.glm sprice.d20nexp,sprice.log10,sprice.root2,biddable,spdiff.cut.fctr,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## All.X.glmnet biddable,sprice.d20nexp,spdiff.cut.fctr,D.ratio.wrds.stop.n.wrds.n,startprice.dcm2.is9,D.wrds.stop.n.log,D.weight.sum.stem.stop.Ratio,D.ratio.weight.sum.wrds.n,.rnorm,startprice.dcm1.is9,storage.fctr,D.chrs.pnct11.n.log,D.chrs.pnct13.n.log,startprice.dgt2.is9,color.fctr,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,prdl.my.fctr,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.n.log,D.terms.post.stem.n.log,D.wrds.unq.n.log,D.terms.post.stop.n.log,cellular.fctr,startprice.dgt1.is9,condition.fctr,sprice.log10,sprice.root2,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## CSM.X.Interact.glmnet .rnorm,D.weight.sum,D.weight.post.stem.sum,D.chrs.pnct13.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,startprice.dcm2.is9,sprice.root2,sprice.log10,startprice.dcm1.is9,D.weight.sum.stem.stop.Ratio,spdiff.cut.fctr*biddable,spdiff.cut.fctr*storage.fctr,spdiff.cut.fctr*prdl.my.fctr,spdiff.cut.fctr*cellular.fctr,spdiff.cut.fctr*D.chrs.pnct11.n.log,spdiff.cut.fctr*condition.fctr,spdiff.cut.fctr*color.fctr,spdiff.cut.fctr*sprice.d20nexp,spdiff.cut.fctr*startprice.dgt2.is9,spdiff.cut.fctr*D.wrds.n.log,spdiff.cut.fctr*D.chrs.n.log,spdiff.cut.fctr*D.chrs.uppr.n.log,spdiff.cut.fctr*D.terms.post.stem.n.log,spdiff.cut.fctr*D.terms.post.stop.n.log,spdiff.cut.fctr*D.wrds.stop.n.log,spdiff.cut.fctr*D.wrds.unq.n.log,spdiff.cut.fctr*startprice.dgt1.is9,spdiff.cut.fctr*D.weight.post.stop.sum,spdiff.cut.fctr*cellular.fctr:carrier.fctr,spdiff.cut.fctr*prdl.my.fctr:.clusterid.fctr
## CSM.X.YeoJohnson.bagEarth spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## CSM.X.center.scale.bagEarth spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## CSM.X.spatialSign.bagEarth spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## max.nTuningRuns max.AUCpROC.fit max.Sens.fit
## MFO.myMFO_classfr 0 0.5000000 1.0000000
## Random.myrandom_classfr 0 0.4859659 0.5164234
## Max.cor.Y.rcv.1X1.glmnet 0 0.7898522 0.8339416
## Max.cor.Y.rcv.3X1.glmnet 25 0.7919708 0.8339416
## Max.cor.Y.rcv.3X3.glmnet 25 0.7919708 0.8339416
## Max.cor.Y.rcv.3X5.glmnet 25 0.7919708 0.8339416
## Max.cor.Y.rcv.5X1.glmnet 25 0.7956204 0.8412409
## Max.cor.Y.rcv.5X3.glmnet 25 0.7956204 0.8412409
## Max.cor.Y.rcv.5X5.glmnet 25 0.7963859 0.8448905
## Max.cor.Y.rcv.1X1.cp.0.rpart 0 0.8281424 0.8978102
## Max.cor.Y.rpart 5 0.8102808 0.8959854
## Interact.High.cor.Y.glmnet 25 0.7970973 0.8886861
## Low.cor.X.glmnet 25 0.8597751 0.8996350
## CSM.X.bagEarth 1 0.8913146 0.9251825
## CSM.X.glm 1 0.8654721 0.9032847
## CSM.X.bayesglm 1 0.8590891 0.9032847
## CSM.X.glmnet 25 0.8545310 0.9069343
## CSM.X.nnet 25 0.8654721 0.9032847
## CSM.X.rpart 5 0.8142763 0.9689781
## CSM.X.gbm 25 0.8910157 0.9416058
## CSM.X.avNNet 25 0.8952788 0.9671533
## CSM.X.rf 5 0.9990876 0.9981752
## CSM.X.earth 5 0.8328001 0.9124088
## RFE.X.glmnet 25 0.8606455 0.8996350
## RFE.X.glm 1 0.8595840 0.8996350
## All.X.glmnet 25 0.8626593 0.9032847
## CSM.X.Interact.glmnet 25 0.8557466 0.9233577
## CSM.X.YeoJohnson.bagEarth 1 0.8834097 0.9178832
## CSM.X.center.scale.bagEarth 1 0.8862983 0.9215328
## CSM.X.spatialSign.bagEarth 1 0.8411555 0.9014599
## max.Spec.fit max.AUCROCR.fit
## MFO.myMFO_classfr 0.0000000 0.5000000
## Random.myrandom_classfr 0.4555085 0.4893217
## Max.cor.Y.rcv.1X1.glmnet 0.7457627 0.8620600
## Max.cor.Y.rcv.3X1.glmnet 0.7500000 0.8621335
## Max.cor.Y.rcv.3X3.glmnet 0.7500000 0.8622147
## Max.cor.Y.rcv.3X5.glmnet 0.7500000 0.8622069
## Max.cor.Y.rcv.5X1.glmnet 0.7500000 0.8621064
## Max.cor.Y.rcv.5X3.glmnet 0.7500000 0.8621064
## Max.cor.Y.rcv.5X5.glmnet 0.7478814 0.8621064
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.7584746 0.8816304
## Max.cor.Y.rpart 0.7245763 0.8387008
## Interact.High.cor.Y.glmnet 0.7055085 0.8639370
## Low.cor.X.glmnet 0.8199153 0.9329264
## CSM.X.bagEarth 0.8574468 0.9584990
## CSM.X.glm 0.8276596 0.9415592
## CSM.X.bayesglm 0.8148936 0.9370632
## CSM.X.glmnet 0.8021277 0.9345318
## CSM.X.nnet 0.8276596 0.9395830
## CSM.X.rpart 0.6595745 0.8400159
## CSM.X.gbm 0.8404255 0.9666078
## CSM.X.avNNet 0.8234043 0.9315849
## CSM.X.rf 1.0000000 0.9999981
## CSM.X.earth 0.7531915 0.9006872
## RFE.X.glmnet 0.8216561 0.9339695
## RFE.X.glm 0.8195329 0.9391534
## All.X.glmnet 0.8220339 0.9355785
## CSM.X.Interact.glmnet 0.7881356 0.9302201
## CSM.X.YeoJohnson.bagEarth 0.8489362 0.9574934
## CSM.X.center.scale.bagEarth 0.8510638 0.9583825
## CSM.X.spatialSign.bagEarth 0.7808511 0.9262968
## opt.prob.threshold.fit max.f.score.fit
## MFO.myMFO_classfr 0.4 0.6327078
## Random.myrandom_classfr 0.4 0.6327078
## Max.cor.Y.rcv.1X1.glmnet 0.4 0.7698745
## Max.cor.Y.rcv.3X1.glmnet 0.5 0.7720829
## Max.cor.Y.rcv.3X3.glmnet 0.5 0.7720829
## Max.cor.Y.rcv.3X5.glmnet 0.5 0.7720829
## Max.cor.Y.rcv.5X1.glmnet 0.5 0.7754655
## Max.cor.Y.rcv.5X3.glmnet 0.5 0.7754655
## Max.cor.Y.rcv.5X5.glmnet 0.5 0.7758242
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.4 0.8102109
## Max.cor.Y.rpart 0.5 0.7853042
## Interact.High.cor.Y.glmnet 0.4 0.7724750
## Low.cor.X.glmnet 0.5 0.8468271
## CSM.X.bagEarth 0.5 0.8818381
## CSM.X.glm 0.4 0.8550420
## CSM.X.bayesglm 0.4 0.8502618
## CSM.X.glmnet 0.4 0.8484211
## CSM.X.nnet 0.4 0.8538622
## CSM.X.rpart 0.9 0.7779172
## CSM.X.gbm 0.4 0.9100418
## CSM.X.avNNet 0.4 0.8878924
## CSM.X.rf 0.5 0.9989373
## CSM.X.earth 0.4 0.8221757
## RFE.X.glmnet 0.4 0.8482328
## RFE.X.glm 0.4 0.8493724
## All.X.glmnet 0.4 0.8503119
## CSM.X.Interact.glmnet 0.4 0.8601036
## CSM.X.YeoJohnson.bagEarth 0.5 0.8730853
## CSM.X.center.scale.bagEarth 0.5 0.8762322
## CSM.X.spatialSign.bagEarth 0.4 0.8299894
## max.Accuracy.fit max.Kappa.fit
## MFO.myMFO_classfr 0.4627451 0.0000000
## Random.myrandom_classfr 0.4627451 0.0000000
## Max.cor.Y.rcv.1X1.glmnet 0.7843137 0.5669826
## Max.cor.Y.rcv.3X1.glmnet 0.7950911 0.5860164
## Max.cor.Y.rcv.3X3.glmnet 0.7954221 0.5866808
## Max.cor.Y.rcv.3X5.glmnet 0.7962655 0.5883400
## Max.cor.Y.rcv.5X1.glmnet 0.7990899 0.5944245
## Max.cor.Y.rcv.5X3.glmnet 0.7964364 0.5888026
## Max.cor.Y.rcv.5X5.glmnet 0.7970687 0.5901123
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.8323529 0.6606905
## Max.cor.Y.rpart 0.7964073 0.5852116
## Interact.High.cor.Y.glmnet 0.8026098 0.5988972
## Low.cor.X.glmnet 0.8349657 0.6666179
## CSM.X.bagEarth 0.8024729 0.6018620
## CSM.X.glm 0.8264355 0.6495141
## CSM.X.bayesglm 0.8293912 0.6549643
## CSM.X.glmnet 0.8323381 0.6607067
## CSM.X.nnet 0.8267681 0.6499515
## CSM.X.rpart 0.8251457 0.6414684
## CSM.X.gbm 0.8323391 0.6607657
## CSM.X.avNNet 0.8271026 0.6493725
## CSM.X.rf 0.8310413 0.6583279
## CSM.X.earth 0.8241614 0.6443967
## RFE.X.glmnet 0.8259789 0.6488269
## RFE.X.glm 0.8217402 0.6404864
## All.X.glmnet 0.8303886 0.6576319
## CSM.X.Interact.glmnet 0.8316958 0.6584576
## CSM.X.YeoJohnson.bagEarth 0.8010417 0.5969627
## CSM.X.center.scale.bagEarth 0.7965221 0.5900610
## CSM.X.spatialSign.bagEarth 0.8149705 0.6248342
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB
## MFO.myMFO_classfr 0.5000000 1.0000000 0.0000000
## Random.myrandom_classfr 0.5064252 0.5480984 0.4647520
## Max.cor.Y.rcv.1X1.glmnet 0.7665376 0.8255034 0.7075718
## Max.cor.Y.rcv.3X1.glmnet 0.7592654 0.8187919 0.6997389
## Max.cor.Y.rcv.3X3.glmnet 0.7603840 0.8210291 0.6997389
## Max.cor.Y.rcv.3X5.glmnet 0.7603840 0.8210291 0.6997389
## Max.cor.Y.rcv.5X1.glmnet 0.7615026 0.8232662 0.6997389
## Max.cor.Y.rcv.5X3.glmnet 0.7615026 0.8232662 0.6997389
## Max.cor.Y.rcv.5X5.glmnet 0.7641135 0.8232662 0.7049608
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.7504950 0.8456376 0.6553525
## Max.cor.Y.rpart 0.7445254 0.8545861 0.6344648
## Interact.High.cor.Y.glmnet 0.7551533 0.8680089 0.6422977
## Low.cor.X.glmnet 0.7801532 0.8187919 0.7415144
## CSM.X.bagEarth 0.8036460 0.8501119 0.7571802
## CSM.X.glm 0.7836958 0.8232662 0.7441253
## CSM.X.bayesglm 0.7902203 0.8389262 0.7415144
## CSM.X.glmnet 0.7937629 0.8434004 0.7441253
## CSM.X.nnet 0.7883570 0.8299776 0.7467363
## CSM.X.rpart 0.7534652 0.9194631 0.5874674
## CSM.X.gbm 0.7573963 0.8411633 0.6736292
## CSM.X.avNNet 0.7797676 0.8859060 0.6736292
## CSM.X.rf 0.7831321 0.8456376 0.7206266
## CSM.X.earth 0.8002815 0.8903803 0.7101828
## RFE.X.glmnet 0.7861198 0.8255034 0.7467363
## RFE.X.glm 0.7851882 0.8210291 0.7493473
## All.X.glmnet 0.7835089 0.8255034 0.7415144
## CSM.X.Interact.glmnet 0.7820106 0.8590604 0.7049608
## CSM.X.YeoJohnson.bagEarth 0.8060701 0.8523490 0.7597911
## CSM.X.center.scale.bagEarth 0.8070017 0.8568233 0.7571802
## CSM.X.spatialSign.bagEarth 0.7896595 0.8456376 0.7336815
## max.AUCROCR.OOB opt.prob.threshold.OOB
## MFO.myMFO_classfr 0.5000000 0.4
## Random.myrandom_classfr 0.4956046 0.4
## Max.cor.Y.rcv.1X1.glmnet 0.8197236 0.4
## Max.cor.Y.rcv.3X1.glmnet 0.8197353 0.4
## Max.cor.Y.rcv.3X3.glmnet 0.8197470 0.4
## Max.cor.Y.rcv.3X5.glmnet 0.8196593 0.4
## Max.cor.Y.rcv.5X1.glmnet 0.8195600 0.4
## Max.cor.Y.rcv.5X3.glmnet 0.8195600 0.4
## Max.cor.Y.rcv.5X5.glmnet 0.8193790 0.4
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.7934621 0.3
## Max.cor.Y.rpart 0.7855854 0.3
## Interact.High.cor.Y.glmnet 0.8189000 0.3
## Low.cor.X.glmnet 0.8727052 0.3
## CSM.X.bagEarth 0.8766246 0.5
## CSM.X.glm 0.8705557 0.5
## CSM.X.bayesglm 0.8720510 0.3
## CSM.X.glmnet 0.8713734 0.4
## CSM.X.nnet 0.8693232 0.5
## CSM.X.rpart 0.7890637 0.3
## CSM.X.gbm 0.8482836 0.3
## CSM.X.avNNet 0.8614202 0.3
## CSM.X.rf 0.8492707 0.3
## CSM.X.earth 0.8533245 0.4
## RFE.X.glmnet 0.8728278 0.3
## RFE.X.glm 0.8700066 0.5
## All.X.glmnet 0.8721444 0.3
## CSM.X.Interact.glmnet 0.8636807 0.4
## CSM.X.YeoJohnson.bagEarth 0.8782951 0.4
## CSM.X.center.scale.bagEarth 0.8764143 0.5
## CSM.X.spatialSign.bagEarth 0.8595102 0.4
## max.f.score.OOB max.Accuracy.OOB
## MFO.myMFO_classfr 0.6314922 0.4614458
## Random.myrandom_classfr 0.6314922 0.4614458
## Max.cor.Y.rcv.1X1.glmnet 0.7535854 0.7722892
## Max.cor.Y.rcv.3X1.glmnet 0.7519182 0.7662651
## Max.cor.Y.rcv.3X3.glmnet 0.7525510 0.7662651
## Max.cor.Y.rcv.3X5.glmnet 0.7515924 0.7650602
## Max.cor.Y.rcv.5X1.glmnet 0.7506361 0.7638554
## Max.cor.Y.rcv.5X3.glmnet 0.7506361 0.7638554
## Max.cor.Y.rcv.5X5.glmnet 0.7512690 0.7638554
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.7313997 0.7433735
## Max.cor.Y.rpart 0.7394270 0.7698795
## Interact.High.cor.Y.glmnet 0.7522698 0.7698795
## Low.cor.X.glmnet 0.7719715 0.7686747
## CSM.X.bagEarth 0.7837838 0.8072289
## CSM.X.glm 0.7630522 0.7867470
## CSM.X.bayesglm 0.7705113 0.7674699
## CSM.X.glmnet 0.7735369 0.7855422
## CSM.X.nnet 0.7677852 0.7915663
## CSM.X.rpart 0.7340426 0.7590361
## CSM.X.gbm 0.7684848 0.7698795
## CSM.X.avNNet 0.7644684 0.7891566
## CSM.X.rf 0.7605295 0.7602410
## CSM.X.earth 0.7878018 0.7987952
## RFE.X.glmnet 0.7668639 0.7626506
## RFE.X.glm 0.7653333 0.7879518
## All.X.glmnet 0.7654028 0.7614458
## CSM.X.Interact.glmnet 0.7898089 0.8012048
## CSM.X.YeoJohnson.bagEarth 0.7888041 0.8000000
## CSM.X.center.scale.bagEarth 0.7869742 0.8108434
## CSM.X.spatialSign.bagEarth 0.7783505 0.7927711
## max.Kappa.OOB inv.elapsedtime.everything
## MFO.myMFO_classfr 0.0000000 3.460207612
## Random.myrandom_classfr 0.0000000 3.436426117
## Max.cor.Y.rcv.1X1.glmnet 0.5419399 1.406469761
## Max.cor.Y.rcv.3X1.glmnet 0.5311363 0.680272109
## Max.cor.Y.rcv.3X3.glmnet 0.5313109 0.487092060
## Max.cor.Y.rcv.3X5.glmnet 0.5289828 0.407497963
## Max.cor.Y.rcv.5X1.glmnet 0.5266555 0.599161174
## Max.cor.Y.rcv.5X3.glmnet 0.5266555 0.410677618
## Max.cor.Y.rcv.5X5.glmnet 0.5268317 0.319284802
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.4862697 1.295336788
## Max.cor.Y.rpart 0.5341327 0.604229607
## Interact.High.cor.Y.glmnet 0.5374385 0.371333086
## Low.cor.X.glmnet 0.5411011 0.332446809
## CSM.X.bagEarth 0.6102577 0.008392853
## CSM.X.glm 0.5694138 0.323834197
## CSM.X.bayesglm 0.5386259 0.194855807
## CSM.X.glmnet 0.5701259 0.067458176
## CSM.X.nnet 0.5789866 0.026818279
## CSM.X.rpart 0.5139178 0.291460216
## CSM.X.gbm 0.5420564 0.081175420
## CSM.X.avNNet 0.5739595 0.008571037
## CSM.X.rf 0.5234042 0.070274069
## CSM.X.earth 0.5967662 0.121270919
## RFE.X.glmnet 0.5294107 0.063901847
## RFE.X.glm 0.5720872 0.344234079
## All.X.glmnet 0.5269348 0.068662455
## CSM.X.Interact.glmnet 0.6014470 0.025638396
## CSM.X.YeoJohnson.bagEarth 0.5991062 0.008570302
## CSM.X.center.scale.bagEarth 0.6173498 0.008005059
## CSM.X.spatialSign.bagEarth 0.5838410 0.035878301
## inv.elapsedtime.final
## MFO.myMFO_classfr 500.00000000
## Random.myrandom_classfr 500.00000000
## Max.cor.Y.rcv.1X1.glmnet 45.45454545
## Max.cor.Y.rcv.3X1.glmnet 58.82352941
## Max.cor.Y.rcv.3X3.glmnet 62.50000000
## Max.cor.Y.rcv.3X5.glmnet 66.66666667
## Max.cor.Y.rcv.5X1.glmnet 62.50000000
## Max.cor.Y.rcv.5X3.glmnet 66.66666667
## Max.cor.Y.rcv.5X5.glmnet 52.63157895
## Max.cor.Y.rcv.1X1.cp.0.rpart 66.66666667
## Max.cor.Y.rpart 62.50000000
## Interact.High.cor.Y.glmnet 10.41666667
## Low.cor.X.glmnet 8.62068966
## CSM.X.bagEarth 0.01708496
## CSM.X.glm 4.29184549
## CSM.X.bayesglm 2.49376559
## CSM.X.glmnet 1.81159420
## CSM.X.nnet 4.62962963
## CSM.X.rpart 12.34567901
## CSM.X.gbm 0.99304866
## CSM.X.avNNet 3.24675325
## CSM.X.rf 0.16840687
## CSM.X.earth 1.06723586
## RFE.X.glmnet 0.62111801
## RFE.X.glm 5.26315789
## All.X.glmnet 1.94174757
## CSM.X.Interact.glmnet 0.41305246
## CSM.X.YeoJohnson.bagEarth 0.01724762
## CSM.X.center.scale.bagEarth 0.01598593
## CSM.X.spatialSign.bagEarth 0.07495128print(myplot_radar(radar_inp_df=plt_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 30. Consider specifying shapes manually if you must have them.## Warning: Removed 480 rows containing missing values (geom_point).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 30. Consider specifying shapes manually if you must have them.
# print(myplot_radar(radar_inp_df=subset(plt_models_df,
# !(mdl_id %in% grep("random|MFO", plt_models_df$id, value=TRUE)))))
# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df,
max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
# Does CI alredy exist ?
var_components <- unlist(strsplit(var, "SD"))
varActul <- paste0(var_components[1], var_components[2])
varUpper <- paste0(var_components[1], "Upper", var_components[2])
varLower <- paste0(var_components[1], "Lower", var_components[2])
if (varUpper %in% names(glb_models_df)) {
warning(varUpper, " already exists in glb_models_df")
# Assuming Lower also exists
next
}
print(sprintf("var:%s", var))
# CI is dependent on sample size in t distribution; df=n-1
glb_models_df[, varUpper] <- glb_models_df[, varActul] +
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
glb_models_df[, varLower] <- glb_models_df[, varActul] -
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}## Warning: max.AccuracyUpper.fit already exists in glb_models_df## [1] "var:max.KappaSD.fit"# Plot metrics with CI
plt_models_df <- glb_models_df[, "id", FALSE]
pltCI_models_df <- glb_models_df[, "id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
var_components <- unlist(strsplit(var, "Upper"))
col_name <- unlist(paste(var_components, collapse=""))
plt_models_df[, col_name] <- glb_models_df[, col_name]
for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
pltCI_models_df[, name] <- glb_models_df[, name]
}
build_statsCI_data <- function(plt_models_df) {
mltd_models_df <- melt(plt_models_df, id.vars="id")
mltd_models_df$data <- sapply(1:nrow(mltd_models_df),
function(row_ix) tail(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]), "[.]")), 1))
mltd_models_df$label <- sapply(1:nrow(mltd_models_df),
function(row_ix) head(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]),
paste0(".", mltd_models_df[row_ix, "data"]))), 1))
#print(mltd_models_df)
return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)
mltdCI_models_df <- melt(pltCI_models_df, id.vars="id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
for (type in c("Upper", "Lower")) {
if (length(var_components <- unlist(strsplit(
as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
#print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
mltdCI_models_df[row_ix, "label"] <- var_components[1]
mltdCI_models_df[row_ix, "data"] <-
unlist(strsplit(var_components[2], "[.]"))[2]
mltdCI_models_df[row_ix, "type"] <- type
break
}
}
}
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable),
timevar="type",
idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")),
direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)
# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
for (type in unique(mltd_models_df$data)) {
var_type <- paste0(var, ".", type)
# if this data is already present, next
if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data,
sep=".")))
next
#print(sprintf("var_type:%s", var_type))
goback_vars <- c(goback_vars, var_type)
}
}
if (length(goback_vars) > 0) {
mltd_goback_df <- build_statsCI_data(glb_models_df[, c("id", goback_vars)])
mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}
# mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("id", "model_method")],
# all.x=TRUE)
png(paste0(glb_out_pfx, "models_bar.png"), width=480*3, height=480*2)
#print(gp <- myplot_bar(mltd_models_df, "id", "value", colorcol_name="model_method") +
print(gp <- myplot_bar(df=mltd_models_df, xcol_name="id", ycol_names="value") +
geom_errorbar(data=mrgdCI_models_df,
mapping=aes(x=mdl_id, ymax=value.Upper, ymin=value.Lower), width=0.5) +
facet_grid(label ~ data, scales="free") +
theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))## Warning: Removed 12 rows containing missing values (geom_errorbar).dev.off()## quartz_off_screen
## 2print(gp)## Warning: Removed 12 rows containing missing values (geom_errorbar).
dsp_models_cols <- c("id",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
# if (glb_is_classification && glb_is_binomial)
# dsp_models_cols <- c(dsp_models_cols, "opt.prob.threshold.OOB")
print(dsp_models_df <- orderBy(get_model_sel_frmla(), glb_models_df)[, dsp_models_cols])## id max.Accuracy.OOB max.AUCROCR.OOB
## 29 CSM.X.center.scale.bagEarth 0.8108434 0.8764143
## 14 CSM.X.bagEarth 0.8072289 0.8766246
## 27 CSM.X.Interact.glmnet 0.8012048 0.8636807
## 28 CSM.X.YeoJohnson.bagEarth 0.8000000 0.8782951
## 23 CSM.X.earth 0.7987952 0.8533245
## 30 CSM.X.spatialSign.bagEarth 0.7927711 0.8595102
## 18 CSM.X.nnet 0.7915663 0.8693232
## 21 CSM.X.avNNet 0.7891566 0.8614202
## 25 RFE.X.glm 0.7879518 0.8700066
## 15 CSM.X.glm 0.7867470 0.8705557
## 17 CSM.X.glmnet 0.7855422 0.8713734
## 3 Max.cor.Y.rcv.1X1.glmnet 0.7722892 0.8197236
## 20 CSM.X.gbm 0.7698795 0.8482836
## 12 Interact.High.cor.Y.glmnet 0.7698795 0.8189000
## 11 Max.cor.Y.rpart 0.7698795 0.7855854
## 13 Low.cor.X.glmnet 0.7686747 0.8727052
## 16 CSM.X.bayesglm 0.7674699 0.8720510
## 5 Max.cor.Y.rcv.3X3.glmnet 0.7662651 0.8197470
## 4 Max.cor.Y.rcv.3X1.glmnet 0.7662651 0.8197353
## 6 Max.cor.Y.rcv.3X5.glmnet 0.7650602 0.8196593
## 7 Max.cor.Y.rcv.5X1.glmnet 0.7638554 0.8195600
## 8 Max.cor.Y.rcv.5X3.glmnet 0.7638554 0.8195600
## 9 Max.cor.Y.rcv.5X5.glmnet 0.7638554 0.8193790
## 24 RFE.X.glmnet 0.7626506 0.8728278
## 26 All.X.glmnet 0.7614458 0.8721444
## 22 CSM.X.rf 0.7602410 0.8492707
## 19 CSM.X.rpart 0.7590361 0.7890637
## 10 Max.cor.Y.rcv.1X1.cp.0.rpart 0.7433735 0.7934621
## 1 MFO.myMFO_classfr 0.4614458 0.5000000
## 2 Random.myrandom_classfr 0.4614458 0.4956046
## max.AUCpROC.OOB max.Accuracy.fit opt.prob.threshold.fit
## 29 0.8070017 0.7965221 0.5
## 14 0.8036460 0.8024729 0.5
## 27 0.7820106 0.8316958 0.4
## 28 0.8060701 0.8010417 0.5
## 23 0.8002815 0.8241614 0.4
## 30 0.7896595 0.8149705 0.4
## 18 0.7883570 0.8267681 0.4
## 21 0.7797676 0.8271026 0.4
## 25 0.7851882 0.8217402 0.4
## 15 0.7836958 0.8264355 0.4
## 17 0.7937629 0.8323381 0.4
## 3 0.7665376 0.7843137 0.4
## 20 0.7573963 0.8323391 0.4
## 12 0.7551533 0.8026098 0.4
## 11 0.7445254 0.7964073 0.5
## 13 0.7801532 0.8349657 0.5
## 16 0.7902203 0.8293912 0.4
## 5 0.7603840 0.7954221 0.5
## 4 0.7592654 0.7950911 0.5
## 6 0.7603840 0.7962655 0.5
## 7 0.7615026 0.7990899 0.5
## 8 0.7615026 0.7964364 0.5
## 9 0.7641135 0.7970687 0.5
## 24 0.7861198 0.8259789 0.4
## 26 0.7835089 0.8303886 0.4
## 22 0.7831321 0.8310413 0.5
## 19 0.7534652 0.8251457 0.9
## 10 0.7504950 0.8323529 0.4
## 1 0.5000000 0.4627451 0.4
## 2 0.5064252 0.4627451 0.4
## opt.prob.threshold.OOB
## 29 0.5
## 14 0.5
## 27 0.4
## 28 0.4
## 23 0.4
## 30 0.4
## 18 0.5
## 21 0.3
## 25 0.5
## 15 0.5
## 17 0.4
## 3 0.4
## 20 0.3
## 12 0.3
## 11 0.3
## 13 0.3
## 16 0.3
## 5 0.4
## 4 0.4
## 6 0.4
## 7 0.4
## 8 0.4
## 9 0.4
## 24 0.3
## 26 0.3
## 22 0.3
## 19 0.3
## 10 0.3
## 1 0.4
## 2 0.4print(myplot_radar(radar_inp_df = dsp_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 30. Consider specifying shapes manually if you must have them.## Warning: Removed 168 rows containing missing values (geom_point).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 30. Consider specifying shapes manually if you must have them.
print("Metrics used for model selection:"); print(get_model_sel_frmla())## [1] "Metrics used for model selection:"## ~-max.Accuracy.OOB - max.AUCROCR.OOB - max.AUCpROC.OOB - max.Accuracy.fit -
## opt.prob.threshold.OOB
## <environment: 0x7fbb180a5550>print(sprintf("Best model id: %s", dsp_models_df[1, "id"]))## [1] "Best model id: CSM.X.center.scale.bagEarth"glb_get_predictions <- function(df, mdl_id, rsp_var_out, prob_threshold_def=NULL, verbose=FALSE) {
mdl <- glb_models_lst[[mdl_id]]
#rsp_var_out <- paste0(rsp_var_out, mdl_id)
rsp_var_out <- paste0(glb_rsp_var, ".predict.")
predct_var_name <- paste0(rsp_var_out, mdl_id)
predct_prob_var_name <- paste0(rsp_var_out, mdl_id, ".prob")
predct_accurate_var_name <- paste0(rsp_var_out, mdl_id, ".accurate")
predct_error_var_name <- paste0(rsp_var_out, mdl_id, ".err")
predct_erabs_var_name <- paste0(rsp_var_out, mdl_id, ".err.abs")
if (glb_is_regression) {
df[, predct_var_name] <- predict(mdl, newdata=df, type="raw")
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
facet_wrap(reformulate(glb_category_var), scales = "free") +
stat_smooth(method="glm"))
df[, predct_error_var_name] <- df[, predct_var_name] - df[, glb_rsp_var]
if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
#facet_wrap(reformulate(glb_category_var), scales = "free") +
stat_smooth(method="auto"))
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
#facet_wrap(reformulate(glb_category_var), scales = "free") +
stat_smooth(method="glm"))
df[, predct_erabs_var_name] <- abs(df[, predct_error_var_name])
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
if (glb_is_classification && glb_is_binomial) {
prob_threshold <- glb_models_df[glb_models_df$id == mdl_id,
"opt.prob.threshold.OOB"]
if (is.null(prob_threshold) || is.na(prob_threshold)) {
warning("Using default probability threshold: ", prob_threshold_def)
if (is.null(prob_threshold <- prob_threshold_def))
stop("Default probability threshold is NULL")
}
df[, predct_prob_var_name] <- predict(mdl, newdata = df, type = "prob")[, 2]
df[, predct_var_name] <-
factor(levels(df[, glb_rsp_var])[
(df[, predct_prob_var_name] >=
prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
# if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
# facet_wrap(reformulate(glb_category_var), scales = "free") +
# stat_smooth(method="glm"))
df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
# if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
# #facet_wrap(reformulate(glb_category_var), scales = "free") +
# stat_smooth(method="auto"))
# if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
# #facet_wrap(reformulate(glb_category_var), scales = "free") +
# stat_smooth(method="glm"))
# if prediction is a TP (true +ve), measure distance from 1.0
tp <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
df[tp, predct_erabs_var_name] <- abs(1 - df[tp, predct_prob_var_name])
#rowIx <- which.max(df[tp, predct_erabs_var_name]); df[tp, c(glb_id_var, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a TN (true -ve), measure distance from 0.0
tn <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
df[tn, predct_erabs_var_name] <- abs(0 - df[tn, predct_prob_var_name])
#rowIx <- which.max(df[tn, predct_erabs_var_name]); df[tn, c(glb_id_var, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a FP (flse +ve), measure distance from 0.0
fp <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
df[fp, predct_erabs_var_name] <- abs(0 - df[fp, predct_prob_var_name])
#rowIx <- which.max(df[fp, predct_erabs_var_name]); df[fp, c(glb_id_var, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a FN (flse -ve), measure distance from 1.0
fn <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
df[fn, predct_erabs_var_name] <- abs(1 - df[fn, predct_prob_var_name])
#rowIx <- which.max(df[fn, predct_erabs_var_name]); df[fn, c(glb_id_var, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
if (glb_is_classification && !glb_is_binomial) {
df[, predct_var_name] <- predict(mdl, newdata = df, type = "raw")
df[, paste0(predct_var_name, ".prob")] <-
predict(mdl, newdata = df, type = "prob")
stop("Multinomial prediction error calculation needs to be implemented...")
}
return(df)
}
#stop(here"); glb_to_sav(); glb_allobs_df <- sav_allobs_df; glb_trnobs_df <- sav_trnobs_df; glb_fitobs_df <- sav_fitobs_df; glb_OOBobs_df <- sav_OOBobs_df; sav_models_df <- glb_models_df; glb_models_df <- sav_models_df; glb_featsimp_df <- sav_featsimp_df
myget_category_stats <- function(obs_df, mdl_id, label) {
require(dplyr)
require(lazyeval)
predct_var_name <- paste0(glb_rsp_var_out, mdl_id)
predct_error_var_name <- paste0(glb_rsp_var_out, mdl_id, ".err.abs")
if (!predct_var_name %in% names(obs_df))
obs_df <- glb_get_predictions(obs_df, mdl_id, glb_rsp_var_out)
tmp_obs_df <- obs_df %>%
dplyr::select_(glb_category_var, glb_rsp_var, predct_var_name, predct_error_var_name)
#dplyr::rename(startprice.log10.predict.RFE.X.glmnet.err=error_abs_OOB)
names(tmp_obs_df)[length(names(tmp_obs_df))] <- paste0("err.abs.", label)
ret_ctgry_df <- tmp_obs_df %>%
dplyr::group_by_(glb_category_var) %>%
dplyr::summarise_(#interp(~sum(abs(var)), var=as.name(glb_rsp_var)),
interp(~sum(var), var=as.name(paste0("err.abs.", label))),
interp(~mean(var), var=as.name(paste0("err.abs.", label))),
interp(~n()))
names(ret_ctgry_df) <- c(glb_category_var,
#paste0(glb_rsp_var, ".abs.", label, ".sum"),
paste0("err.abs.", label, ".sum"),
paste0("err.abs.", label, ".mean"),
paste0(".n.", label))
ret_ctgry_df <- dplyr::ungroup(ret_ctgry_df)
#colSums(ret_ctgry_df[, -grep(glb_category_var, names(ret_ctgry_df))])
return(ret_ctgry_df)
}
#print(colSums((ctgry_df <- myget_category_stats(obs_df=glb_fitobs_df, mdl_id="", label="fit"))[, -grep(glb_category_var, names(ctgry_df))]))
if (!is.null(glb_mdl_ensemble)) {
fit.models_2_chunk_df <- myadd_chunk(fit.models_2_chunk_df,
paste0("fit.models_2_", mdl_id_pfx), major.inc = TRUE,
label.minor = "ensemble")
mdl_id_pfx <- "Ensemble"
if (#(glb_is_regression) |
((glb_is_classification) & (!glb_is_binomial)))
stop("Ensemble models not implemented yet for multinomial classification")
mygetEnsembleAutoMdlIds <- function() {
tmp_models_df <- orderBy(get_model_sel_frmla(), glb_models_df)
row.names(tmp_models_df) <- tmp_models_df$id
mdl_threshold_pos <-
min(which(grepl("MFO|Random|Baseline", tmp_models_df$id))) - 1
mdlIds <- tmp_models_df$id[1:mdl_threshold_pos]
return(mdlIds[!grepl("Ensemble", mdlIds)])
}
if (glb_mdl_ensemble == "auto") {
glb_mdl_ensemble <- mygetEnsembleAutoMdlIds()
mdl_id_pfx <- paste0(mdl_id_pfx, ".auto")
} else if (grepl("^%<d-%", glb_mdl_ensemble)) {
glb_mdl_ensemble <- eval(parse(text =
str_trim(unlist(strsplit(glb_mdl_ensemble, "%<d-%"))[2])))
}
for (mdl_id in glb_mdl_ensemble) {
if (!(mdl_id %in% names(glb_models_lst))) {
warning("Model ", mdl_id, " in glb_model_ensemble not found !")
next
}
glb_fitobs_df <- glb_get_predictions(df = glb_fitobs_df, mdl_id,
glb_rsp_var_out)
glb_OOBobs_df <- glb_get_predictions(df = glb_OOBobs_df, mdl_id,
glb_rsp_var_out)
}
#mdl_id_pfx <- "Ensemble.RFE"; mdlId <- paste0(mdl_id_pfx, ".glmnet")
#glb_mdl_ensemble <- gsub(glb_rsp_var_out, "", grep("RFE\\.X\\.(?!Interact)", row.names(glb_featsimp_df), perl = TRUE, value = TRUE), fixed = TRUE)
#varImp(glb_models_lst[[mdlId]])
#cor_df <- data.frame(cor=cor(glb_fitobs_df[, glb_rsp_var], glb_fitobs_df[, paste(glb_rsp_var_out, glb_mdl_ensemble)], use="pairwise.complete.obs"))
#glb_fitobs_df <- glb_get_predictions(df=glb_fitobs_df, "Ensemble.glmnet", glb_rsp_var_out);print(colSums((ctgry_df <- myget_category_stats(obs_df=glb_fitobs_df, mdl_id="Ensemble.glmnet", label="fit"))[, -grep(glb_category_var, names(ctgry_df))]))
### bid0_sp
# Better than MFO; models.n=28; min.RMSE.fit=0.0521233; err.abs.fit.sum=7.3631895
# old: Top x from auto; models.n= 5; min.RMSE.fit=0.06311047; err.abs.fit.sum=9.5937080
# RFE only ; models.n=16; min.RMSE.fit=0.05148588; err.abs.fit.sum=7.2875091
# RFE subset only ;models.n= 5; min.RMSE.fit=0.06040702; err.abs.fit.sum=9.059088
# RFE subset only ;models.n= 9; min.RMSE.fit=0.05933167; err.abs.fit.sum=8.7421288
# RFE subset only ;models.n=15; min.RMSE.fit=0.0584607; err.abs.fit.sum=8.5902066
# RFE subset only ;models.n=17; min.RMSE.fit=0.05496899; err.abs.fit.sum=8.0170431
# RFE subset only ;models.n=18; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
# RFE subset only ;models.n=16; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
### bid0_sp
### bid1_sp
# "auto"; err.abs.fit.sum=76.699774; min.RMSE.fit=0.2186429
# "RFE.X.*"; err.abs.fit.sum=; min.RMSE.fit=0.221114
### bid1_sp
indep_vars <- paste(glb_rsp_var_out, glb_mdl_ensemble, sep = "")
if (glb_is_classification)
indep_vars <- paste(indep_vars, ".prob", sep = "")
# Some models in glb_mdl_ensemble might not be fitted e.g. RFE.X.Interact
indep_vars <- intersect(indep_vars, names(glb_fitobs_df))
# indep_vars <- grep(glb_rsp_var_out, names(glb_fitobs_df), fixed=TRUE, value=TRUE)
# if (glb_is_regression)
# indep_vars <- indep_vars[!grepl("(err\\.abs|accurate)$", indep_vars)]
# if (glb_is_classification && glb_is_binomial)
# indep_vars <- grep("prob$", indep_vars, value=TRUE) else
# indep_vars <- indep_vars[!grepl("err$", indep_vars)]
#rfe_fit_ens_results <- myrun_rfe(glb_fitobs_df, indep_vars)
for (method in c("glm", "glmnet")) {
for (trainControlMethod in
c("boot", "boot632", "cv", "repeatedcv"
#, "LOOCV" # tuneLength * nrow(fitDF)
, "LGOCV", "adaptive_cv"
#, "adaptive_boot" #error: adaptive$min should be less than 3
#, "adaptive_LGOCV" #error: adaptive$min should be less than 3
)) {
#sav_models_df <- glb_models_df; all.equal(sav_models_df, glb_models_df)
#glb_models_df <- sav_models_df; print(glb_models_df$id)
if ((method == "glm") && (trainControlMethod != "repeatedcv"))
# glm used only to identify outliers
next
ret_lst <- myfit_mdl(
mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = paste0(mdl_id_pfx, ".", trainControlMethod),
type = glb_model_type, tune.df = NULL,
trainControl.method = trainControlMethod,
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = method)),
indep_vars = indep_vars, rsp_var = glb_rsp_var,
fit_df = glb_fitobs_df, OOB_df = glb_OOBobs_df)
}
}
dsp_models_df <- get_dsp_models_df()
}## label step_major step_minor label_minor bgn end
## 1 fit.models_2_bgn 1 0 setup 1340.900 1352.211
## 2 fit.models_2_CSM.X 2 0 ensemble 1352.212 NA
## elapsed
## 1 11.311
## 2 NA## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## [1] "fitting model: Ensemble.repeatedcv.glm"
## [1] " indep_vars: sold.fctr.predict.CSM.X.center.scale.bagEarth.prob,sold.fctr.predict.CSM.X.bagEarth.prob,sold.fctr.predict.CSM.X.Interact.glmnet.prob,sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.prob,sold.fctr.predict.CSM.X.earth.prob,sold.fctr.predict.CSM.X.spatialSign.bagEarth.prob,sold.fctr.predict.CSM.X.nnet.prob,sold.fctr.predict.CSM.X.avNNet.prob,sold.fctr.predict.RFE.X.glm.prob,sold.fctr.predict.CSM.X.glm.prob,sold.fctr.predict.CSM.X.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.prob,sold.fctr.predict.CSM.X.gbm.prob,sold.fctr.predict.Interact.High.cor.Y.glmnet.prob,sold.fctr.predict.Max.cor.Y.rpart.prob,sold.fctr.predict.Low.cor.X.glmnet.prob,sold.fctr.predict.CSM.X.bayesglm.prob,sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.prob,sold.fctr.predict.RFE.X.glmnet.prob,sold.fctr.predict.All.X.glmnet.prob,sold.fctr.predict.CSM.X.rpart.prob,sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.prob"
## + Fold1.Rep1: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold1.Rep1: parameter=none
## + Fold2.Rep1: parameter=none## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## - Fold2.Rep1: parameter=none
## + Fold3.Rep1: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold3.Rep1: parameter=none
## + Fold1.Rep2: parameter=none## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## - Fold1.Rep2: parameter=none
## + Fold2.Rep2: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold2.Rep2: parameter=none
## + Fold3.Rep2: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold3.Rep2: parameter=none
## + Fold1.Rep3: parameter=none## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## - Fold1.Rep3: parameter=none
## + Fold2.Rep3: parameter=none## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold2.Rep3: parameter=none
## + Fold3.Rep3: parameter=none## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## - Fold3.Rep3: parameter=none
## Aggregating results
## Fitting final model on full training set## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -8.49 0.00 0.00 0.00 8.49
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error
## (Intercept) 1.058e+14 4.985e+08
## sold.fctr.predict.All.X.glmnet.prob 4.090e+15 2.070e+08
## sold.fctr.predict.CSM.X.Interact.glmnet.prob 7.001e+14 4.285e+07
## sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.prob -3.033e+14 9.193e+07
## sold.fctr.predict.CSM.X.avNNet.prob 1.367e+15 1.984e+07
## sold.fctr.predict.CSM.X.bagEarth.prob -6.291e+14 1.463e+08
## sold.fctr.predict.CSM.X.bayesglm.prob -5.107e+15 2.056e+08
## sold.fctr.predict.CSM.X.center.scale.bagEarth.prob 2.714e+15 1.502e+08
## sold.fctr.predict.CSM.X.earth.prob -1.515e+15 2.938e+07
## sold.fctr.predict.CSM.X.gbm.prob 1.465e+15 2.947e+07
## sold.fctr.predict.CSM.X.glm.prob 4.007e+15 1.610e+08
## sold.fctr.predict.CSM.X.glmnet.prob 8.270e+13 1.370e+08
## sold.fctr.predict.CSM.X.nnet.prob 1.440e+15 7.088e+07
## sold.fctr.predict.CSM.X.rpart.prob -5.160e+14 2.604e+07
## sold.fctr.predict.CSM.X.spatialSign.bagEarth.prob 1.127e+15 3.017e+07
## sold.fctr.predict.Interact.High.cor.Y.glmnet.prob 9.988e+13 4.408e+07
## sold.fctr.predict.Low.cor.X.glmnet.prob -1.884e+15 9.047e+07
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.prob -6.583e+13 2.214e+07
## sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.prob -1.315e+17 3.529e+09
## sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.prob 1.527e+19 9.982e+11
## sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.prob -2.004e+19 3.150e+12
## sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.prob -9.239e+18 3.742e+12
## sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.prob 2.116e+19 1.949e+12
## sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.prob NA NA
## sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.prob -7.013e+18 3.671e+11
## sold.fctr.predict.Max.cor.Y.rpart.prob -4.367e+14 3.736e+07
## sold.fctr.predict.RFE.X.glm.prob -4.499e+15 1.629e+08
## sold.fctr.predict.RFE.X.glmnet.prob 3.900e+14 2.236e+08
## z value Pr(>|z|)
## (Intercept) 212257 <2e-16 ***
## sold.fctr.predict.All.X.glmnet.prob 19756772 <2e-16 ***
## sold.fctr.predict.CSM.X.Interact.glmnet.prob 16336347 <2e-16 ***
## sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.prob -3299104 <2e-16 ***
## sold.fctr.predict.CSM.X.avNNet.prob 68876026 <2e-16 ***
## sold.fctr.predict.CSM.X.bagEarth.prob -4298936 <2e-16 ***
## sold.fctr.predict.CSM.X.bayesglm.prob -24835504 <2e-16 ***
## sold.fctr.predict.CSM.X.center.scale.bagEarth.prob 18068200 <2e-16 ***
## sold.fctr.predict.CSM.X.earth.prob -51574124 <2e-16 ***
## sold.fctr.predict.CSM.X.gbm.prob 49716103 <2e-16 ***
## sold.fctr.predict.CSM.X.glm.prob 24889736 <2e-16 ***
## sold.fctr.predict.CSM.X.glmnet.prob 603775 <2e-16 ***
## sold.fctr.predict.CSM.X.nnet.prob 20321794 <2e-16 ***
## sold.fctr.predict.CSM.X.rpart.prob -19812843 <2e-16 ***
## sold.fctr.predict.CSM.X.spatialSign.bagEarth.prob 37367726 <2e-16 ***
## sold.fctr.predict.Interact.High.cor.Y.glmnet.prob 2265800 <2e-16 ***
## sold.fctr.predict.Low.cor.X.glmnet.prob -20828358 <2e-16 ***
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.prob -2973368 <2e-16 ***
## sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.prob -37261187 <2e-16 ***
## sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.prob 15295120 <2e-16 ***
## sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.prob -6363846 <2e-16 ***
## sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.prob -2468991 <2e-16 ***
## sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.prob 10857461 <2e-16 ***
## sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.prob NA NA
## sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.prob -19106281 <2e-16 ***
## sold.fctr.predict.Max.cor.Y.rpart.prob -11691647 <2e-16 ***
## sold.fctr.predict.RFE.X.glm.prob -27612837 <2e-16 ***
## sold.fctr.predict.RFE.X.glmnet.prob 1743862 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1408.4 on 1019 degrees of freedom
## Residual deviance: 6776.2 on 993 degrees of freedom
## AIC: 6830.2
##
## Number of Fisher Scoring iterations: 25## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## sold.fctr sold.fctr.predict.Ensemble.repeatedcv.glm.N
## 1 N 490
## 2 Y 36
## sold.fctr.predict.Ensemble.repeatedcv.glm.Y
## 1 58
## 2 436
## Prediction
## Reference N Y
## N 490 58
## Y 36 436
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.078431e-01 8.152544e-01 8.884049e-01 9.248868e-01 5.372549e-01
## AccuracyPValue McnemarPValue
## 3.933811e-147 3.031236e-02## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## sold.fctr sold.fctr.predict.Ensemble.repeatedcv.glm.N
## 1 N 329
## 2 Y 96
## sold.fctr.predict.Ensemble.repeatedcv.glm.Y
## 1 118
## 2 287
## Prediction
## Reference N Y
## N 329 118
## Y 96 287
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.421687e-01 4.833774e-01 7.109795e-01 7.716270e-01 5.385542e-01
## AccuracyPValue McnemarPValue
## 1.066763e-33 1.511360e-01
## id
## 1 Ensemble.repeatedcv.glm
## feats
## 1 sold.fctr.predict.CSM.X.center.scale.bagEarth.prob,sold.fctr.predict.CSM.X.bagEarth.prob,sold.fctr.predict.CSM.X.Interact.glmnet.prob,sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.prob,sold.fctr.predict.CSM.X.earth.prob,sold.fctr.predict.CSM.X.spatialSign.bagEarth.prob,sold.fctr.predict.CSM.X.nnet.prob,sold.fctr.predict.CSM.X.avNNet.prob,sold.fctr.predict.RFE.X.glm.prob,sold.fctr.predict.CSM.X.glm.prob,sold.fctr.predict.CSM.X.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.prob,sold.fctr.predict.CSM.X.gbm.prob,sold.fctr.predict.Interact.High.cor.Y.glmnet.prob,sold.fctr.predict.Max.cor.Y.rpart.prob,sold.fctr.predict.Low.cor.X.glmnet.prob,sold.fctr.predict.CSM.X.bayesglm.prob,sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.prob,sold.fctr.predict.RFE.X.glmnet.prob,sold.fctr.predict.All.X.glmnet.prob,sold.fctr.predict.CSM.X.rpart.prob,sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.prob
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.849 0.087
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.9089447 0.8941606 0.9237288 0.9089447
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.9 0.9026915 0.691823
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8884049 0.9248868 0.3789803
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.7426826 0.7360179 0.7493473 0.7426826
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.9 0.7284264 0.7421687
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7109795 0.771627 0.4833774
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.2246707 0.4452524
## [1] "fitting model: Ensemble.boot.glmnet"
## [1] " indep_vars: sold.fctr.predict.CSM.X.center.scale.bagEarth.prob,sold.fctr.predict.CSM.X.bagEarth.prob,sold.fctr.predict.CSM.X.Interact.glmnet.prob,sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.prob,sold.fctr.predict.CSM.X.earth.prob,sold.fctr.predict.CSM.X.spatialSign.bagEarth.prob,sold.fctr.predict.CSM.X.nnet.prob,sold.fctr.predict.CSM.X.avNNet.prob,sold.fctr.predict.RFE.X.glm.prob,sold.fctr.predict.CSM.X.glm.prob,sold.fctr.predict.CSM.X.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.prob,sold.fctr.predict.CSM.X.gbm.prob,sold.fctr.predict.Interact.High.cor.Y.glmnet.prob,sold.fctr.predict.Max.cor.Y.rpart.prob,sold.fctr.predict.Low.cor.X.glmnet.prob,sold.fctr.predict.CSM.X.bayesglm.prob,sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.prob,sold.fctr.predict.RFE.X.glmnet.prob,sold.fctr.predict.All.X.glmnet.prob,sold.fctr.predict.CSM.X.rpart.prob,sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.prob"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.55, lambda = 0.000389 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0(mdl_id_pfx, : model's bestTune found at an extreme
## of tuneGrid for parameter: lambda
## Length Class Mode
## a0 100 -none- numeric
## beta 2700 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 27 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## -3.5102749
## sold.fctr.predict.CSM.X.Interact.glmnet.prob
## 5.0184000
## sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.prob
## 1.2601894
## sold.fctr.predict.CSM.X.avNNet.prob
## 6.1524872
## sold.fctr.predict.CSM.X.bagEarth.prob
## 2.4865492
## sold.fctr.predict.CSM.X.bayesglm.prob
## -0.7418255
## sold.fctr.predict.CSM.X.center.scale.bagEarth.prob
## 2.3572996
## sold.fctr.predict.CSM.X.earth.prob
## -2.1848119
## sold.fctr.predict.CSM.X.gbm.prob
## 7.5311789
## sold.fctr.predict.CSM.X.glm.prob
## 0.1876119
## sold.fctr.predict.CSM.X.glmnet.prob
## -5.5882944
## sold.fctr.predict.CSM.X.nnet.prob
## 1.0723187
## sold.fctr.predict.CSM.X.rpart.prob
## -3.1547555
## sold.fctr.predict.CSM.X.spatialSign.bagEarth.prob
## -0.8843460
## sold.fctr.predict.Interact.High.cor.Y.glmnet.prob
## -2.5535118
## sold.fctr.predict.Low.cor.X.glmnet.prob
## -1.9650765
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.prob
## 3.3930970
## sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.prob
## -0.8800897
## sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.prob
## -0.9311873
## sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.prob
## -0.6554502
## sold.fctr.predict.Max.cor.Y.rpart.prob
## -1.6670607
## sold.fctr.predict.RFE.X.glm.prob
## -0.1142283
## sold.fctr.predict.RFE.X.glmnet.prob
## -0.4097455
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## -3.5160233
## sold.fctr.predict.CSM.X.Interact.glmnet.prob
## 5.1319991
## sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.prob
## 1.2146033
## sold.fctr.predict.CSM.X.avNNet.prob
## 6.1908543
## sold.fctr.predict.CSM.X.bagEarth.prob
## 2.5044513
## sold.fctr.predict.CSM.X.bayesglm.prob
## -0.7229158
## sold.fctr.predict.CSM.X.center.scale.bagEarth.prob
## 2.4110640
## sold.fctr.predict.CSM.X.earth.prob
## -2.1750136
## sold.fctr.predict.CSM.X.gbm.prob
## 7.5646405
## sold.fctr.predict.CSM.X.glm.prob
## 0.3343920
## sold.fctr.predict.CSM.X.glmnet.prob
## -5.9605314
## sold.fctr.predict.CSM.X.nnet.prob
## 1.1756415
## sold.fctr.predict.CSM.X.rpart.prob
## -3.1921777
## sold.fctr.predict.CSM.X.spatialSign.bagEarth.prob
## -0.8983756
## sold.fctr.predict.Interact.High.cor.Y.glmnet.prob
## -2.5466000
## sold.fctr.predict.Low.cor.X.glmnet.prob
## -2.0296125
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.prob
## 3.4306994
## sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.prob
## -0.8810577
## sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.prob
## -0.9523131
## sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.prob
## -0.6717007
## sold.fctr.predict.Max.cor.Y.rpart.prob
## -1.6824856
## sold.fctr.predict.RFE.X.glm.prob
## -0.2659699
## sold.fctr.predict.RFE.X.glmnet.prob
## -0.2359036
## sold.fctr sold.fctr.predict.Ensemble.boot.glmnet.N
## 1 N 527
## 2 Y 42
## sold.fctr.predict.Ensemble.boot.glmnet.Y
## 1 21
## 2 430
## Prediction
## Reference N Y
## N 527 21
## Y 42 430
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.382353e-01 8.753965e-01 9.216645e-01 9.522155e-01 5.372549e-01
## AccuracyPValue McnemarPValue
## 1.346206e-178 1.174338e-02
## sold.fctr sold.fctr.predict.Ensemble.boot.glmnet.N
## 1 N 323
## 2 Y 72
## sold.fctr.predict.Ensemble.boot.glmnet.Y
## 1 124
## 2 311
## Prediction
## Reference N Y
## N 323 124
## Y 72 311
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.638554e-01 5.294594e-01 7.334512e-01 7.923725e-01 5.385542e-01
## AccuracyPValue McnemarPValue
## 3.171799e-41 2.696285e-04
## id
## 1 Ensemble.boot.glmnet
## feats
## 1 sold.fctr.predict.CSM.X.center.scale.bagEarth.prob,sold.fctr.predict.CSM.X.bagEarth.prob,sold.fctr.predict.CSM.X.Interact.glmnet.prob,sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.prob,sold.fctr.predict.CSM.X.earth.prob,sold.fctr.predict.CSM.X.spatialSign.bagEarth.prob,sold.fctr.predict.CSM.X.nnet.prob,sold.fctr.predict.CSM.X.avNNet.prob,sold.fctr.predict.RFE.X.glm.prob,sold.fctr.predict.CSM.X.glm.prob,sold.fctr.predict.CSM.X.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.prob,sold.fctr.predict.CSM.X.gbm.prob,sold.fctr.predict.Interact.High.cor.Y.glmnet.prob,sold.fctr.predict.Max.cor.Y.rpart.prob,sold.fctr.predict.Low.cor.X.glmnet.prob,sold.fctr.predict.CSM.X.bayesglm.prob,sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.prob,sold.fctr.predict.RFE.X.glmnet.prob,sold.fctr.predict.All.X.glmnet.prob,sold.fctr.predict.CSM.X.rpart.prob,sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.prob
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 3.984 0.418
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.9363479 0.9616788 0.9110169 0.9774217
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.9317443 0.9367195
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.9216645 0.9522155 0.8734191
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.7678431 0.8255034 0.7101828 0.8366715
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.2 0.7603912 0.7638554
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7334512 0.7923725 0.5294594
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01110131 0.0220355
## [1] "fitting model: Ensemble.boot632.glmnet"
## [1] " indep_vars: sold.fctr.predict.CSM.X.center.scale.bagEarth.prob,sold.fctr.predict.CSM.X.bagEarth.prob,sold.fctr.predict.CSM.X.Interact.glmnet.prob,sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.prob,sold.fctr.predict.CSM.X.earth.prob,sold.fctr.predict.CSM.X.spatialSign.bagEarth.prob,sold.fctr.predict.CSM.X.nnet.prob,sold.fctr.predict.CSM.X.avNNet.prob,sold.fctr.predict.RFE.X.glm.prob,sold.fctr.predict.CSM.X.glm.prob,sold.fctr.predict.CSM.X.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.prob,sold.fctr.predict.CSM.X.gbm.prob,sold.fctr.predict.Interact.High.cor.Y.glmnet.prob,sold.fctr.predict.Max.cor.Y.rpart.prob,sold.fctr.predict.Low.cor.X.glmnet.prob,sold.fctr.predict.CSM.X.bayesglm.prob,sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.prob,sold.fctr.predict.RFE.X.glmnet.prob,sold.fctr.predict.All.X.glmnet.prob,sold.fctr.predict.CSM.X.rpart.prob,sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.prob"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.55, lambda = 0.000389 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0(mdl_id_pfx, : model's bestTune found at an extreme
## of tuneGrid for parameter: lambda
## Length Class Mode
## a0 100 -none- numeric
## beta 2700 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 27 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## -3.5102749
## sold.fctr.predict.CSM.X.Interact.glmnet.prob
## 5.0184000
## sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.prob
## 1.2601894
## sold.fctr.predict.CSM.X.avNNet.prob
## 6.1524872
## sold.fctr.predict.CSM.X.bagEarth.prob
## 2.4865492
## sold.fctr.predict.CSM.X.bayesglm.prob
## -0.7418255
## sold.fctr.predict.CSM.X.center.scale.bagEarth.prob
## 2.3572996
## sold.fctr.predict.CSM.X.earth.prob
## -2.1848119
## sold.fctr.predict.CSM.X.gbm.prob
## 7.5311789
## sold.fctr.predict.CSM.X.glm.prob
## 0.1876119
## sold.fctr.predict.CSM.X.glmnet.prob
## -5.5882944
## sold.fctr.predict.CSM.X.nnet.prob
## 1.0723187
## sold.fctr.predict.CSM.X.rpart.prob
## -3.1547555
## sold.fctr.predict.CSM.X.spatialSign.bagEarth.prob
## -0.8843460
## sold.fctr.predict.Interact.High.cor.Y.glmnet.prob
## -2.5535118
## sold.fctr.predict.Low.cor.X.glmnet.prob
## -1.9650765
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.prob
## 3.3930970
## sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.prob
## -0.8800897
## sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.prob
## -0.9311873
## sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.prob
## -0.6554502
## sold.fctr.predict.Max.cor.Y.rpart.prob
## -1.6670607
## sold.fctr.predict.RFE.X.glm.prob
## -0.1142283
## sold.fctr.predict.RFE.X.glmnet.prob
## -0.4097455
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## -3.5160233
## sold.fctr.predict.CSM.X.Interact.glmnet.prob
## 5.1319991
## sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.prob
## 1.2146033
## sold.fctr.predict.CSM.X.avNNet.prob
## 6.1908543
## sold.fctr.predict.CSM.X.bagEarth.prob
## 2.5044513
## sold.fctr.predict.CSM.X.bayesglm.prob
## -0.7229158
## sold.fctr.predict.CSM.X.center.scale.bagEarth.prob
## 2.4110640
## sold.fctr.predict.CSM.X.earth.prob
## -2.1750136
## sold.fctr.predict.CSM.X.gbm.prob
## 7.5646405
## sold.fctr.predict.CSM.X.glm.prob
## 0.3343920
## sold.fctr.predict.CSM.X.glmnet.prob
## -5.9605314
## sold.fctr.predict.CSM.X.nnet.prob
## 1.1756415
## sold.fctr.predict.CSM.X.rpart.prob
## -3.1921777
## sold.fctr.predict.CSM.X.spatialSign.bagEarth.prob
## -0.8983756
## sold.fctr.predict.Interact.High.cor.Y.glmnet.prob
## -2.5466000
## sold.fctr.predict.Low.cor.X.glmnet.prob
## -2.0296125
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.prob
## 3.4306994
## sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.prob
## -0.8810577
## sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.prob
## -0.9523131
## sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.prob
## -0.6717007
## sold.fctr.predict.Max.cor.Y.rpart.prob
## -1.6824856
## sold.fctr.predict.RFE.X.glm.prob
## -0.2659699
## sold.fctr.predict.RFE.X.glmnet.prob
## -0.2359036
## sold.fctr sold.fctr.predict.Ensemble.boot632.glmnet.N
## 1 N 527
## 2 Y 42
## sold.fctr.predict.Ensemble.boot632.glmnet.Y
## 1 21
## 2 430
## Prediction
## Reference N Y
## N 527 21
## Y 42 430
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.382353e-01 8.753965e-01 9.216645e-01 9.522155e-01 5.372549e-01
## AccuracyPValue McnemarPValue
## 1.346206e-178 1.174338e-02
## sold.fctr sold.fctr.predict.Ensemble.boot632.glmnet.N
## 1 N 323
## 2 Y 72
## sold.fctr.predict.Ensemble.boot632.glmnet.Y
## 1 124
## 2 311
## Prediction
## Reference N Y
## N 323 124
## Y 72 311
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.638554e-01 5.294594e-01 7.334512e-01 7.923725e-01 5.385542e-01
## AccuracyPValue McnemarPValue
## 3.171799e-41 2.696285e-04
## id
## 1 Ensemble.boot632.glmnet
## feats
## 1 sold.fctr.predict.CSM.X.center.scale.bagEarth.prob,sold.fctr.predict.CSM.X.bagEarth.prob,sold.fctr.predict.CSM.X.Interact.glmnet.prob,sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.prob,sold.fctr.predict.CSM.X.earth.prob,sold.fctr.predict.CSM.X.spatialSign.bagEarth.prob,sold.fctr.predict.CSM.X.nnet.prob,sold.fctr.predict.CSM.X.avNNet.prob,sold.fctr.predict.RFE.X.glm.prob,sold.fctr.predict.CSM.X.glm.prob,sold.fctr.predict.CSM.X.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.prob,sold.fctr.predict.CSM.X.gbm.prob,sold.fctr.predict.Interact.High.cor.Y.glmnet.prob,sold.fctr.predict.Max.cor.Y.rpart.prob,sold.fctr.predict.Low.cor.X.glmnet.prob,sold.fctr.predict.CSM.X.bayesglm.prob,sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.prob,sold.fctr.predict.RFE.X.glmnet.prob,sold.fctr.predict.All.X.glmnet.prob,sold.fctr.predict.CSM.X.rpart.prob,sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.prob
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 4.906 0.424
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.9363479 0.9616788 0.9110169 0.9774217
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.9317443 0.9372772
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.9216645 0.9522155 0.8741465
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.7678431 0.8255034 0.7101828 0.8366715
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.2 0.7603912 0.7638554
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7334512 0.7923725 0.5294594
## max.AccuracySD.fit max.KappaSD.fit max.AccuracyApparent.fit
## 1 0.01110131 0.0220355 0.9382353
## max.KappaApparent.fit
## 1 0.8753965
## [1] "fitting model: Ensemble.cv.glmnet"
## [1] " indep_vars: sold.fctr.predict.CSM.X.center.scale.bagEarth.prob,sold.fctr.predict.CSM.X.bagEarth.prob,sold.fctr.predict.CSM.X.Interact.glmnet.prob,sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.prob,sold.fctr.predict.CSM.X.earth.prob,sold.fctr.predict.CSM.X.spatialSign.bagEarth.prob,sold.fctr.predict.CSM.X.nnet.prob,sold.fctr.predict.CSM.X.avNNet.prob,sold.fctr.predict.RFE.X.glm.prob,sold.fctr.predict.CSM.X.glm.prob,sold.fctr.predict.CSM.X.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.prob,sold.fctr.predict.CSM.X.gbm.prob,sold.fctr.predict.Interact.High.cor.Y.glmnet.prob,sold.fctr.predict.Max.cor.Y.rpart.prob,sold.fctr.predict.Low.cor.X.glmnet.prob,sold.fctr.predict.CSM.X.bayesglm.prob,sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.prob,sold.fctr.predict.RFE.X.glmnet.prob,sold.fctr.predict.All.X.glmnet.prob,sold.fctr.predict.CSM.X.rpart.prob,sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.prob"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.000389 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0(mdl_id_pfx, : model's bestTune found at an extreme
## of tuneGrid for parameter: alpha## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0(mdl_id_pfx, : model's bestTune found at an extreme
## of tuneGrid for parameter: lambda
## Length Class Mode
## a0 100 -none- numeric
## beta 2700 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 27 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## -3.3819910
## sold.fctr.predict.All.X.glmnet.prob
## 0.6730661
## sold.fctr.predict.CSM.X.Interact.glmnet.prob
## 5.0225313
## sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.prob
## 1.5880296
## sold.fctr.predict.CSM.X.avNNet.prob
## 5.9904332
## sold.fctr.predict.CSM.X.bagEarth.prob
## 2.3087045
## sold.fctr.predict.CSM.X.bayesglm.prob
## -1.8445009
## sold.fctr.predict.CSM.X.center.scale.bagEarth.prob
## 2.3383299
## sold.fctr.predict.CSM.X.earth.prob
## -2.2690042
## sold.fctr.predict.CSM.X.gbm.prob
## 7.2992023
## sold.fctr.predict.CSM.X.glm.prob
## 0.9808838
## sold.fctr.predict.CSM.X.glmnet.prob
## -4.6731164
## sold.fctr.predict.CSM.X.nnet.prob
## 1.0979658
## sold.fctr.predict.CSM.X.rpart.prob
## -3.0287920
## sold.fctr.predict.CSM.X.spatialSign.bagEarth.prob
## -0.9472116
## sold.fctr.predict.Interact.High.cor.Y.glmnet.prob
## -2.3227159
## sold.fctr.predict.Low.cor.X.glmnet.prob
## -1.7041723
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.prob
## 3.4078588
## sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.prob
## 0.2134230
## sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.prob
## -0.9726886
## sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.prob
## -0.8804873
## sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.prob
## -0.7020241
## sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.prob
## -0.2880315
## sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.prob
## -0.3395680
## sold.fctr.predict.Max.cor.Y.rpart.prob
## -1.6721123
## sold.fctr.predict.RFE.X.glm.prob
## -1.0140539
## sold.fctr.predict.RFE.X.glmnet.prob
## -0.8177911
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)"
## [2] "sold.fctr.predict.All.X.glmnet.prob"
## [3] "sold.fctr.predict.CSM.X.Interact.glmnet.prob"
## [4] "sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.prob"
## [5] "sold.fctr.predict.CSM.X.avNNet.prob"
## [6] "sold.fctr.predict.CSM.X.bagEarth.prob"
## [7] "sold.fctr.predict.CSM.X.bayesglm.prob"
## [8] "sold.fctr.predict.CSM.X.center.scale.bagEarth.prob"
## [9] "sold.fctr.predict.CSM.X.earth.prob"
## [10] "sold.fctr.predict.CSM.X.gbm.prob"
## [11] "sold.fctr.predict.CSM.X.glm.prob"
## [12] "sold.fctr.predict.CSM.X.glmnet.prob"
## [13] "sold.fctr.predict.CSM.X.nnet.prob"
## [14] "sold.fctr.predict.CSM.X.rpart.prob"
## [15] "sold.fctr.predict.CSM.X.spatialSign.bagEarth.prob"
## [16] "sold.fctr.predict.Interact.High.cor.Y.glmnet.prob"
## [17] "sold.fctr.predict.Low.cor.X.glmnet.prob"
## [18] "sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.prob"
## [19] "sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.prob"
## [20] "sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.prob"
## [21] "sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.prob"
## [22] "sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.prob"
## [23] "sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.prob"
## [24] "sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.prob"
## [25] "sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.prob"
## [26] "sold.fctr.predict.Max.cor.Y.rpart.prob"
## [27] "sold.fctr.predict.RFE.X.glm.prob"
## [28] "sold.fctr.predict.RFE.X.glmnet.prob"
## sold.fctr sold.fctr.predict.Ensemble.cv.glmnet.N
## 1 N 519
## 2 Y 34
## sold.fctr.predict.Ensemble.cv.glmnet.Y
## 1 29
## 2 438
## Prediction
## Reference N Y
## N 519 29
## Y 34 438
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.382353e-01 8.756896e-01 9.216645e-01 9.522155e-01 5.372549e-01
## AccuracyPValue McnemarPValue
## 1.346206e-178 6.142947e-01
## sold.fctr sold.fctr.predict.Ensemble.cv.glmnet.N
## 1 N 328
## 2 Y 72
## sold.fctr.predict.Ensemble.cv.glmnet.Y
## 1 119
## 2 311
## Prediction
## Reference N Y
## N 328 119
## Y 72 311
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.698795e-01 5.410382e-01 7.397113e-01 7.981170e-01 5.385542e-01
## AccuracyPValue McnemarPValue
## 1.766677e-43 8.733282e-04
## id
## 1 Ensemble.cv.glmnet
## feats
## 1 sold.fctr.predict.CSM.X.center.scale.bagEarth.prob,sold.fctr.predict.CSM.X.bagEarth.prob,sold.fctr.predict.CSM.X.Interact.glmnet.prob,sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.prob,sold.fctr.predict.CSM.X.earth.prob,sold.fctr.predict.CSM.X.spatialSign.bagEarth.prob,sold.fctr.predict.CSM.X.nnet.prob,sold.fctr.predict.CSM.X.avNNet.prob,sold.fctr.predict.RFE.X.glm.prob,sold.fctr.predict.CSM.X.glm.prob,sold.fctr.predict.CSM.X.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.prob,sold.fctr.predict.CSM.X.gbm.prob,sold.fctr.predict.Interact.High.cor.Y.glmnet.prob,sold.fctr.predict.Max.cor.Y.rpart.prob,sold.fctr.predict.Low.cor.X.glmnet.prob,sold.fctr.predict.CSM.X.bayesglm.prob,sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.prob,sold.fctr.predict.RFE.X.glmnet.prob,sold.fctr.predict.All.X.glmnet.prob,sold.fctr.predict.CSM.X.rpart.prob,sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.prob
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 3.448 0.186
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.9363479 0.9616788 0.9110169 0.9773212
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.9329073 0.9362655
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.9216645 0.9522155 0.8713739
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.7667245 0.8232662 0.7101828 0.8382077
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.2 0.7650677 0.7698795
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7397113 0.798117 0.5410382
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.007502706 0.01525824
## [1] "fitting model: Ensemble.repeatedcv.glmnet"
## [1] " indep_vars: sold.fctr.predict.CSM.X.center.scale.bagEarth.prob,sold.fctr.predict.CSM.X.bagEarth.prob,sold.fctr.predict.CSM.X.Interact.glmnet.prob,sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.prob,sold.fctr.predict.CSM.X.earth.prob,sold.fctr.predict.CSM.X.spatialSign.bagEarth.prob,sold.fctr.predict.CSM.X.nnet.prob,sold.fctr.predict.CSM.X.avNNet.prob,sold.fctr.predict.RFE.X.glm.prob,sold.fctr.predict.CSM.X.glm.prob,sold.fctr.predict.CSM.X.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.prob,sold.fctr.predict.CSM.X.gbm.prob,sold.fctr.predict.Interact.High.cor.Y.glmnet.prob,sold.fctr.predict.Max.cor.Y.rpart.prob,sold.fctr.predict.Low.cor.X.glmnet.prob,sold.fctr.predict.CSM.X.bayesglm.prob,sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.prob,sold.fctr.predict.RFE.X.glmnet.prob,sold.fctr.predict.All.X.glmnet.prob,sold.fctr.predict.CSM.X.rpart.prob,sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.prob"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.000389 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0(mdl_id_pfx, : model's bestTune found at an extreme
## of tuneGrid for parameter: alpha## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0(mdl_id_pfx, : model's bestTune found at an extreme
## of tuneGrid for parameter: lambda
## Length Class Mode
## a0 100 -none- numeric
## beta 2700 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 27 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## -3.3819910
## sold.fctr.predict.All.X.glmnet.prob
## 0.6730661
## sold.fctr.predict.CSM.X.Interact.glmnet.prob
## 5.0225313
## sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.prob
## 1.5880296
## sold.fctr.predict.CSM.X.avNNet.prob
## 5.9904332
## sold.fctr.predict.CSM.X.bagEarth.prob
## 2.3087045
## sold.fctr.predict.CSM.X.bayesglm.prob
## -1.8445009
## sold.fctr.predict.CSM.X.center.scale.bagEarth.prob
## 2.3383299
## sold.fctr.predict.CSM.X.earth.prob
## -2.2690042
## sold.fctr.predict.CSM.X.gbm.prob
## 7.2992023
## sold.fctr.predict.CSM.X.glm.prob
## 0.9808838
## sold.fctr.predict.CSM.X.glmnet.prob
## -4.6731164
## sold.fctr.predict.CSM.X.nnet.prob
## 1.0979658
## sold.fctr.predict.CSM.X.rpart.prob
## -3.0287920
## sold.fctr.predict.CSM.X.spatialSign.bagEarth.prob
## -0.9472116
## sold.fctr.predict.Interact.High.cor.Y.glmnet.prob
## -2.3227159
## sold.fctr.predict.Low.cor.X.glmnet.prob
## -1.7041723
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.prob
## 3.4078588
## sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.prob
## 0.2134230
## sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.prob
## -0.9726886
## sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.prob
## -0.8804873
## sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.prob
## -0.7020241
## sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.prob
## -0.2880315
## sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.prob
## -0.3395680
## sold.fctr.predict.Max.cor.Y.rpart.prob
## -1.6721123
## sold.fctr.predict.RFE.X.glm.prob
## -1.0140539
## sold.fctr.predict.RFE.X.glmnet.prob
## -0.8177911
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)"
## [2] "sold.fctr.predict.All.X.glmnet.prob"
## [3] "sold.fctr.predict.CSM.X.Interact.glmnet.prob"
## [4] "sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.prob"
## [5] "sold.fctr.predict.CSM.X.avNNet.prob"
## [6] "sold.fctr.predict.CSM.X.bagEarth.prob"
## [7] "sold.fctr.predict.CSM.X.bayesglm.prob"
## [8] "sold.fctr.predict.CSM.X.center.scale.bagEarth.prob"
## [9] "sold.fctr.predict.CSM.X.earth.prob"
## [10] "sold.fctr.predict.CSM.X.gbm.prob"
## [11] "sold.fctr.predict.CSM.X.glm.prob"
## [12] "sold.fctr.predict.CSM.X.glmnet.prob"
## [13] "sold.fctr.predict.CSM.X.nnet.prob"
## [14] "sold.fctr.predict.CSM.X.rpart.prob"
## [15] "sold.fctr.predict.CSM.X.spatialSign.bagEarth.prob"
## [16] "sold.fctr.predict.Interact.High.cor.Y.glmnet.prob"
## [17] "sold.fctr.predict.Low.cor.X.glmnet.prob"
## [18] "sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.prob"
## [19] "sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.prob"
## [20] "sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.prob"
## [21] "sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.prob"
## [22] "sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.prob"
## [23] "sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.prob"
## [24] "sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.prob"
## [25] "sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.prob"
## [26] "sold.fctr.predict.Max.cor.Y.rpart.prob"
## [27] "sold.fctr.predict.RFE.X.glm.prob"
## [28] "sold.fctr.predict.RFE.X.glmnet.prob"
## sold.fctr sold.fctr.predict.Ensemble.repeatedcv.glmnet.N
## 1 N 519
## 2 Y 34
## sold.fctr.predict.Ensemble.repeatedcv.glmnet.Y
## 1 29
## 2 438
## Prediction
## Reference N Y
## N 519 29
## Y 34 438
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.382353e-01 8.756896e-01 9.216645e-01 9.522155e-01 5.372549e-01
## AccuracyPValue McnemarPValue
## 1.346206e-178 6.142947e-01
## sold.fctr sold.fctr.predict.Ensemble.repeatedcv.glmnet.N
## 1 N 328
## 2 Y 72
## sold.fctr.predict.Ensemble.repeatedcv.glmnet.Y
## 1 119
## 2 311
## Prediction
## Reference N Y
## N 328 119
## Y 72 311
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.698795e-01 5.410382e-01 7.397113e-01 7.981170e-01 5.385542e-01
## AccuracyPValue McnemarPValue
## 1.766677e-43 8.733282e-04
## id
## 1 Ensemble.repeatedcv.glmnet
## feats
## 1 sold.fctr.predict.CSM.X.center.scale.bagEarth.prob,sold.fctr.predict.CSM.X.bagEarth.prob,sold.fctr.predict.CSM.X.Interact.glmnet.prob,sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.prob,sold.fctr.predict.CSM.X.earth.prob,sold.fctr.predict.CSM.X.spatialSign.bagEarth.prob,sold.fctr.predict.CSM.X.nnet.prob,sold.fctr.predict.CSM.X.avNNet.prob,sold.fctr.predict.RFE.X.glm.prob,sold.fctr.predict.CSM.X.glm.prob,sold.fctr.predict.CSM.X.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.prob,sold.fctr.predict.CSM.X.gbm.prob,sold.fctr.predict.Interact.High.cor.Y.glmnet.prob,sold.fctr.predict.Max.cor.Y.rpart.prob,sold.fctr.predict.Low.cor.X.glmnet.prob,sold.fctr.predict.CSM.X.bayesglm.prob,sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.prob,sold.fctr.predict.RFE.X.glmnet.prob,sold.fctr.predict.All.X.glmnet.prob,sold.fctr.predict.CSM.X.rpart.prob,sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.prob
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 6.492 0.188
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.9363479 0.9616788 0.9110169 0.9773212
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.9329073 0.9329967
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.9216645 0.9522155 0.8647102
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.7667245 0.8232662 0.7101828 0.8382077
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.2 0.7650677 0.7698795
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7397113 0.798117 0.5410382
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01466196 0.02985178
## [1] "fitting model: Ensemble.LGOCV.glmnet"
## [1] " indep_vars: sold.fctr.predict.CSM.X.center.scale.bagEarth.prob,sold.fctr.predict.CSM.X.bagEarth.prob,sold.fctr.predict.CSM.X.Interact.glmnet.prob,sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.prob,sold.fctr.predict.CSM.X.earth.prob,sold.fctr.predict.CSM.X.spatialSign.bagEarth.prob,sold.fctr.predict.CSM.X.nnet.prob,sold.fctr.predict.CSM.X.avNNet.prob,sold.fctr.predict.RFE.X.glm.prob,sold.fctr.predict.CSM.X.glm.prob,sold.fctr.predict.CSM.X.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.prob,sold.fctr.predict.CSM.X.gbm.prob,sold.fctr.predict.Interact.High.cor.Y.glmnet.prob,sold.fctr.predict.Max.cor.Y.rpart.prob,sold.fctr.predict.Low.cor.X.glmnet.prob,sold.fctr.predict.CSM.X.bayesglm.prob,sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.prob,sold.fctr.predict.RFE.X.glmnet.prob,sold.fctr.predict.All.X.glmnet.prob,sold.fctr.predict.CSM.X.rpart.prob,sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.prob"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.000389 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0(mdl_id_pfx, : model's bestTune found at an extreme
## of tuneGrid for parameter: alpha## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0(mdl_id_pfx, : model's bestTune found at an extreme
## of tuneGrid for parameter: lambda
## Length Class Mode
## a0 100 -none- numeric
## beta 2700 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 27 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## -3.3819910
## sold.fctr.predict.All.X.glmnet.prob
## 0.6730661
## sold.fctr.predict.CSM.X.Interact.glmnet.prob
## 5.0225313
## sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.prob
## 1.5880296
## sold.fctr.predict.CSM.X.avNNet.prob
## 5.9904332
## sold.fctr.predict.CSM.X.bagEarth.prob
## 2.3087045
## sold.fctr.predict.CSM.X.bayesglm.prob
## -1.8445009
## sold.fctr.predict.CSM.X.center.scale.bagEarth.prob
## 2.3383299
## sold.fctr.predict.CSM.X.earth.prob
## -2.2690042
## sold.fctr.predict.CSM.X.gbm.prob
## 7.2992023
## sold.fctr.predict.CSM.X.glm.prob
## 0.9808838
## sold.fctr.predict.CSM.X.glmnet.prob
## -4.6731164
## sold.fctr.predict.CSM.X.nnet.prob
## 1.0979658
## sold.fctr.predict.CSM.X.rpart.prob
## -3.0287920
## sold.fctr.predict.CSM.X.spatialSign.bagEarth.prob
## -0.9472116
## sold.fctr.predict.Interact.High.cor.Y.glmnet.prob
## -2.3227159
## sold.fctr.predict.Low.cor.X.glmnet.prob
## -1.7041723
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.prob
## 3.4078588
## sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.prob
## 0.2134230
## sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.prob
## -0.9726886
## sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.prob
## -0.8804873
## sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.prob
## -0.7020241
## sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.prob
## -0.2880315
## sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.prob
## -0.3395680
## sold.fctr.predict.Max.cor.Y.rpart.prob
## -1.6721123
## sold.fctr.predict.RFE.X.glm.prob
## -1.0140539
## sold.fctr.predict.RFE.X.glmnet.prob
## -0.8177911
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)"
## [2] "sold.fctr.predict.All.X.glmnet.prob"
## [3] "sold.fctr.predict.CSM.X.Interact.glmnet.prob"
## [4] "sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.prob"
## [5] "sold.fctr.predict.CSM.X.avNNet.prob"
## [6] "sold.fctr.predict.CSM.X.bagEarth.prob"
## [7] "sold.fctr.predict.CSM.X.bayesglm.prob"
## [8] "sold.fctr.predict.CSM.X.center.scale.bagEarth.prob"
## [9] "sold.fctr.predict.CSM.X.earth.prob"
## [10] "sold.fctr.predict.CSM.X.gbm.prob"
## [11] "sold.fctr.predict.CSM.X.glm.prob"
## [12] "sold.fctr.predict.CSM.X.glmnet.prob"
## [13] "sold.fctr.predict.CSM.X.nnet.prob"
## [14] "sold.fctr.predict.CSM.X.rpart.prob"
## [15] "sold.fctr.predict.CSM.X.spatialSign.bagEarth.prob"
## [16] "sold.fctr.predict.Interact.High.cor.Y.glmnet.prob"
## [17] "sold.fctr.predict.Low.cor.X.glmnet.prob"
## [18] "sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.prob"
## [19] "sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.prob"
## [20] "sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.prob"
## [21] "sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.prob"
## [22] "sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.prob"
## [23] "sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.prob"
## [24] "sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.prob"
## [25] "sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.prob"
## [26] "sold.fctr.predict.Max.cor.Y.rpart.prob"
## [27] "sold.fctr.predict.RFE.X.glm.prob"
## [28] "sold.fctr.predict.RFE.X.glmnet.prob"
## sold.fctr sold.fctr.predict.Ensemble.LGOCV.glmnet.N
## 1 N 519
## 2 Y 34
## sold.fctr.predict.Ensemble.LGOCV.glmnet.Y
## 1 29
## 2 438
## Prediction
## Reference N Y
## N 519 29
## Y 34 438
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.382353e-01 8.756896e-01 9.216645e-01 9.522155e-01 5.372549e-01
## AccuracyPValue McnemarPValue
## 1.346206e-178 6.142947e-01
## sold.fctr sold.fctr.predict.Ensemble.LGOCV.glmnet.N
## 1 N 328
## 2 Y 72
## sold.fctr.predict.Ensemble.LGOCV.glmnet.Y
## 1 119
## 2 311
## Prediction
## Reference N Y
## N 328 119
## Y 72 311
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.698795e-01 5.410382e-01 7.397113e-01 7.981170e-01 5.385542e-01
## AccuracyPValue McnemarPValue
## 1.766677e-43 8.733282e-04
## id
## 1 Ensemble.LGOCV.glmnet
## feats
## 1 sold.fctr.predict.CSM.X.center.scale.bagEarth.prob,sold.fctr.predict.CSM.X.bagEarth.prob,sold.fctr.predict.CSM.X.Interact.glmnet.prob,sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.prob,sold.fctr.predict.CSM.X.earth.prob,sold.fctr.predict.CSM.X.spatialSign.bagEarth.prob,sold.fctr.predict.CSM.X.nnet.prob,sold.fctr.predict.CSM.X.avNNet.prob,sold.fctr.predict.RFE.X.glm.prob,sold.fctr.predict.CSM.X.glm.prob,sold.fctr.predict.CSM.X.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.prob,sold.fctr.predict.CSM.X.gbm.prob,sold.fctr.predict.Interact.High.cor.Y.glmnet.prob,sold.fctr.predict.Max.cor.Y.rpart.prob,sold.fctr.predict.Low.cor.X.glmnet.prob,sold.fctr.predict.CSM.X.bayesglm.prob,sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.prob,sold.fctr.predict.RFE.X.glmnet.prob,sold.fctr.predict.All.X.glmnet.prob,sold.fctr.predict.CSM.X.rpart.prob,sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.prob
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 3.448 0.19
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.9363479 0.9616788 0.9110169 0.9773212
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.9329073 0.9346405
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.9216645 0.9522155 0.8680537
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.7667245 0.8232662 0.7101828 0.8382077
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.2 0.7650677 0.7698795
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7397113 0.798117 0.5410382
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.02363809 0.04795752
## [1] "fitting model: Ensemble.adaptive_cv.glmnet"
## [1] " indep_vars: sold.fctr.predict.CSM.X.center.scale.bagEarth.prob,sold.fctr.predict.CSM.X.bagEarth.prob,sold.fctr.predict.CSM.X.Interact.glmnet.prob,sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.prob,sold.fctr.predict.CSM.X.earth.prob,sold.fctr.predict.CSM.X.spatialSign.bagEarth.prob,sold.fctr.predict.CSM.X.nnet.prob,sold.fctr.predict.CSM.X.avNNet.prob,sold.fctr.predict.RFE.X.glm.prob,sold.fctr.predict.CSM.X.glm.prob,sold.fctr.predict.CSM.X.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.prob,sold.fctr.predict.CSM.X.gbm.prob,sold.fctr.predict.Interact.High.cor.Y.glmnet.prob,sold.fctr.predict.Max.cor.Y.rpart.prob,sold.fctr.predict.Low.cor.X.glmnet.prob,sold.fctr.predict.CSM.X.bayesglm.prob,sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.prob,sold.fctr.predict.RFE.X.glmnet.prob,sold.fctr.predict.All.X.glmnet.prob,sold.fctr.predict.CSM.X.rpart.prob,sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.prob"
## o 1 model was eliminated due to linear dependencies
## o 15 eliminated;9 remain
## o 1 eliminated;8 remain
## o no models eliminated; 8 remain
## o 1 eliminated;7 remain
## o no models eliminated; 7 remain
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.000389 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0(mdl_id_pfx, : model's bestTune found at an extreme
## of tuneGrid for parameter: alpha## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0(mdl_id_pfx, : model's bestTune found at an extreme
## of tuneGrid for parameter: lambda
## Length Class Mode
## a0 100 -none- numeric
## beta 2700 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 27 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## -3.3819910
## sold.fctr.predict.All.X.glmnet.prob
## 0.6730661
## sold.fctr.predict.CSM.X.Interact.glmnet.prob
## 5.0225313
## sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.prob
## 1.5880296
## sold.fctr.predict.CSM.X.avNNet.prob
## 5.9904332
## sold.fctr.predict.CSM.X.bagEarth.prob
## 2.3087045
## sold.fctr.predict.CSM.X.bayesglm.prob
## -1.8445009
## sold.fctr.predict.CSM.X.center.scale.bagEarth.prob
## 2.3383299
## sold.fctr.predict.CSM.X.earth.prob
## -2.2690042
## sold.fctr.predict.CSM.X.gbm.prob
## 7.2992023
## sold.fctr.predict.CSM.X.glm.prob
## 0.9808838
## sold.fctr.predict.CSM.X.glmnet.prob
## -4.6731164
## sold.fctr.predict.CSM.X.nnet.prob
## 1.0979658
## sold.fctr.predict.CSM.X.rpart.prob
## -3.0287920
## sold.fctr.predict.CSM.X.spatialSign.bagEarth.prob
## -0.9472116
## sold.fctr.predict.Interact.High.cor.Y.glmnet.prob
## -2.3227159
## sold.fctr.predict.Low.cor.X.glmnet.prob
## -1.7041723
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.prob
## 3.4078588
## sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.prob
## 0.2134230
## sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.prob
## -0.9726886
## sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.prob
## -0.8804873
## sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.prob
## -0.7020241
## sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.prob
## -0.2880315
## sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.prob
## -0.3395680
## sold.fctr.predict.Max.cor.Y.rpart.prob
## -1.6721123
## sold.fctr.predict.RFE.X.glm.prob
## -1.0140539
## sold.fctr.predict.RFE.X.glmnet.prob
## -0.8177911
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)"
## [2] "sold.fctr.predict.All.X.glmnet.prob"
## [3] "sold.fctr.predict.CSM.X.Interact.glmnet.prob"
## [4] "sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.prob"
## [5] "sold.fctr.predict.CSM.X.avNNet.prob"
## [6] "sold.fctr.predict.CSM.X.bagEarth.prob"
## [7] "sold.fctr.predict.CSM.X.bayesglm.prob"
## [8] "sold.fctr.predict.CSM.X.center.scale.bagEarth.prob"
## [9] "sold.fctr.predict.CSM.X.earth.prob"
## [10] "sold.fctr.predict.CSM.X.gbm.prob"
## [11] "sold.fctr.predict.CSM.X.glm.prob"
## [12] "sold.fctr.predict.CSM.X.glmnet.prob"
## [13] "sold.fctr.predict.CSM.X.nnet.prob"
## [14] "sold.fctr.predict.CSM.X.rpart.prob"
## [15] "sold.fctr.predict.CSM.X.spatialSign.bagEarth.prob"
## [16] "sold.fctr.predict.Interact.High.cor.Y.glmnet.prob"
## [17] "sold.fctr.predict.Low.cor.X.glmnet.prob"
## [18] "sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.prob"
## [19] "sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.prob"
## [20] "sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.prob"
## [21] "sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.prob"
## [22] "sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.prob"
## [23] "sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.prob"
## [24] "sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.prob"
## [25] "sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.prob"
## [26] "sold.fctr.predict.Max.cor.Y.rpart.prob"
## [27] "sold.fctr.predict.RFE.X.glm.prob"
## [28] "sold.fctr.predict.RFE.X.glmnet.prob"
## sold.fctr sold.fctr.predict.Ensemble.adaptive_cv.glmnet.N
## 1 N 519
## 2 Y 34
## sold.fctr.predict.Ensemble.adaptive_cv.glmnet.Y
## 1 29
## 2 438
## Prediction
## Reference N Y
## N 519 29
## Y 34 438
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.382353e-01 8.756896e-01 9.216645e-01 9.522155e-01 5.372549e-01
## AccuracyPValue McnemarPValue
## 1.346206e-178 6.142947e-01
## sold.fctr sold.fctr.predict.Ensemble.adaptive_cv.glmnet.N
## 1 N 328
## 2 Y 72
## sold.fctr.predict.Ensemble.adaptive_cv.glmnet.Y
## 1 119
## 2 311
## Prediction
## Reference N Y
## N 328 119
## Y 72 311
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.698795e-01 5.410382e-01 7.397113e-01 7.981170e-01 5.385542e-01
## AccuracyPValue McnemarPValue
## 1.766677e-43 8.733282e-04
## id
## 1 Ensemble.adaptive_cv.glmnet
## feats
## 1 sold.fctr.predict.CSM.X.center.scale.bagEarth.prob,sold.fctr.predict.CSM.X.bagEarth.prob,sold.fctr.predict.CSM.X.Interact.glmnet.prob,sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.prob,sold.fctr.predict.CSM.X.earth.prob,sold.fctr.predict.CSM.X.spatialSign.bagEarth.prob,sold.fctr.predict.CSM.X.nnet.prob,sold.fctr.predict.CSM.X.avNNet.prob,sold.fctr.predict.RFE.X.glm.prob,sold.fctr.predict.CSM.X.glm.prob,sold.fctr.predict.CSM.X.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.prob,sold.fctr.predict.CSM.X.gbm.prob,sold.fctr.predict.Interact.High.cor.Y.glmnet.prob,sold.fctr.predict.Max.cor.Y.rpart.prob,sold.fctr.predict.Low.cor.X.glmnet.prob,sold.fctr.predict.CSM.X.bayesglm.prob,sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.prob,sold.fctr.predict.RFE.X.glmnet.prob,sold.fctr.predict.All.X.glmnet.prob,sold.fctr.predict.CSM.X.rpart.prob,sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.prob
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 8.828 0.189
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.9363479 0.9616788 0.9110169 0.9773212
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.9329073 0.9329967
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.9216645 0.9522155 0.8647102
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.7667245 0.8232662 0.7101828 0.8382077
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.2 0.7650677 0.7698795
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7397113 0.798117 0.5410382
## max.AccuracySD.fit max.KappaSD.fit max..B.fit
## 1 0.01466196 0.02985178 9
## [1] "Cross Validation issues:"## Warning in get_dsp_models_df(): Cross Validation issues:## MFO.myMFO_classfr Random.myrandom_classfr
## 0 0
## Max.cor.Y.rcv.1X1.glmnet Max.cor.Y.rcv.1X1.cp.0.rpart
## 0 0
## CSM.X.bagEarth CSM.X.YeoJohnson.bagEarth
## 1 1
## CSM.X.center.scale.bagEarth CSM.X.spatialSign.bagEarth
## 1 1
if (is.null(glb_sel_mdl_id))
glb_sel_mdl_id <- dsp_models_df[1, "id"] else
print(sprintf("User specified selection: %s", glb_sel_mdl_id)) ## [1] "User specified selection: Ensemble.repeatedcv.glmnet"myprint_mdl(glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]])
## Length Class Mode
## a0 100 -none- numeric
## beta 2700 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 27 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## -3.3819910
## sold.fctr.predict.All.X.glmnet.prob
## 0.6730661
## sold.fctr.predict.CSM.X.Interact.glmnet.prob
## 5.0225313
## sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.prob
## 1.5880296
## sold.fctr.predict.CSM.X.avNNet.prob
## 5.9904332
## sold.fctr.predict.CSM.X.bagEarth.prob
## 2.3087045
## sold.fctr.predict.CSM.X.bayesglm.prob
## -1.8445009
## sold.fctr.predict.CSM.X.center.scale.bagEarth.prob
## 2.3383299
## sold.fctr.predict.CSM.X.earth.prob
## -2.2690042
## sold.fctr.predict.CSM.X.gbm.prob
## 7.2992023
## sold.fctr.predict.CSM.X.glm.prob
## 0.9808838
## sold.fctr.predict.CSM.X.glmnet.prob
## -4.6731164
## sold.fctr.predict.CSM.X.nnet.prob
## 1.0979658
## sold.fctr.predict.CSM.X.rpart.prob
## -3.0287920
## sold.fctr.predict.CSM.X.spatialSign.bagEarth.prob
## -0.9472116
## sold.fctr.predict.Interact.High.cor.Y.glmnet.prob
## -2.3227159
## sold.fctr.predict.Low.cor.X.glmnet.prob
## -1.7041723
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.prob
## 3.4078588
## sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.prob
## 0.2134230
## sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.prob
## -0.9726886
## sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.prob
## -0.8804873
## sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.prob
## -0.7020241
## sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.prob
## -0.2880315
## sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.prob
## -0.3395680
## sold.fctr.predict.Max.cor.Y.rpart.prob
## -1.6721123
## sold.fctr.predict.RFE.X.glm.prob
## -1.0140539
## sold.fctr.predict.RFE.X.glmnet.prob
## -0.8177911
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)"
## [2] "sold.fctr.predict.All.X.glmnet.prob"
## [3] "sold.fctr.predict.CSM.X.Interact.glmnet.prob"
## [4] "sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.prob"
## [5] "sold.fctr.predict.CSM.X.avNNet.prob"
## [6] "sold.fctr.predict.CSM.X.bagEarth.prob"
## [7] "sold.fctr.predict.CSM.X.bayesglm.prob"
## [8] "sold.fctr.predict.CSM.X.center.scale.bagEarth.prob"
## [9] "sold.fctr.predict.CSM.X.earth.prob"
## [10] "sold.fctr.predict.CSM.X.gbm.prob"
## [11] "sold.fctr.predict.CSM.X.glm.prob"
## [12] "sold.fctr.predict.CSM.X.glmnet.prob"
## [13] "sold.fctr.predict.CSM.X.nnet.prob"
## [14] "sold.fctr.predict.CSM.X.rpart.prob"
## [15] "sold.fctr.predict.CSM.X.spatialSign.bagEarth.prob"
## [16] "sold.fctr.predict.Interact.High.cor.Y.glmnet.prob"
## [17] "sold.fctr.predict.Low.cor.X.glmnet.prob"
## [18] "sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.prob"
## [19] "sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.prob"
## [20] "sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.prob"
## [21] "sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.prob"
## [22] "sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.prob"
## [23] "sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.prob"
## [24] "sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.prob"
## [25] "sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.prob"
## [26] "sold.fctr.predict.Max.cor.Y.rpart.prob"
## [27] "sold.fctr.predict.RFE.X.glm.prob"
## [28] "sold.fctr.predict.RFE.X.glmnet.prob"## [1] TRUE#stop(here"); glb_to_sav()
# From here to save(), this should all be in one function
# these are executed in the same seq twice more:
# fit.data.training & predict.data.new chunks
print(sprintf("%s fit prediction diagnostics:", glb_sel_mdl_id))## [1] "Ensemble.repeatedcv.glmnet fit prediction diagnostics:"glb_fitobs_df <- glb_get_predictions(df=glb_fitobs_df, mdl_id=glb_sel_mdl_id,
rsp_var_out=glb_rsp_var_out)
print(sprintf("%s OOB prediction diagnostics:", glb_sel_mdl_id))## [1] "Ensemble.repeatedcv.glmnet OOB prediction diagnostics:"glb_OOBobs_df <- glb_get_predictions(df = glb_OOBobs_df, mdl_id = glb_sel_mdl_id,
rsp_var_out = glb_rsp_var_out)
glb_featsimp_df <-
myget_feats_importance(mdl=glb_sel_mdl, featsimp_df=NULL)
glb_featsimp_df[, paste0(glb_sel_mdl_id, ".importance")] <- glb_featsimp_df$importance
#mdl_id <-"RFE.X.glmnet"; glb_featsimp_df <- myget_feats_importance(glb_models_lst[[mdl_id]], glb_featsimp_df); glb_featsimp_df[, paste0(mdl_id, ".importance")] <- glb_featsimp_df$importance; print(glb_featsimp_df)
#print(head(sbst_featsimp_df <- subset(glb_featsimp_df, is.na(RFE.X.glmnet.importance) | (abs(RFE.X.YeoJohnson.glmnet.importance - RFE.X.glmnet.importance) > 0.0001), select=-importance)))
#print(orderBy(~ -cor.y.abs, subset(glb_feats_df, id %in% c(row.names(sbst_featsimp_df), "startprice.dcm1.is9", "D.weight.post.stop.sum"))))
print(glb_featsimp_df)## importance
## sold.fctr.predict.CSM.X.gbm.prob 100.00000
## sold.fctr.predict.CSM.X.avNNet.prob 89.06837
## sold.fctr.predict.CSM.X.Interact.glmnet.prob 80.98388
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.prob 67.49716
## sold.fctr.predict.CSM.X.center.scale.bagEarth.prob 58.56381
## sold.fctr.predict.CSM.X.bagEarth.prob 58.31636
## sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.prob 52.29685
## sold.fctr.predict.CSM.X.nnet.prob 48.20355
## sold.fctr.predict.CSM.X.glm.prob 47.22561
## sold.fctr.predict.All.X.glmnet.prob 44.65453
## sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.prob 40.81531
## sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.prob 39.03268
## sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.prob 36.62686
## sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.prob 36.19640
## sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.prob 33.16895
## sold.fctr.predict.RFE.X.glmnet.prob 32.20199
## sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.prob 31.67832
## sold.fctr.predict.CSM.X.spatialSign.bagEarth.prob 31.12100
## sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.prob 30.90820
## sold.fctr.predict.RFE.X.glm.prob 30.56269
## sold.fctr.predict.Max.cor.Y.rpart.prob 25.06619
## sold.fctr.predict.Low.cor.X.glmnet.prob 24.79841
## sold.fctr.predict.CSM.X.bayesglm.prob 23.62630
## sold.fctr.predict.CSM.X.earth.prob 20.08059
## sold.fctr.predict.Interact.High.cor.Y.glmnet.prob 19.63196
## sold.fctr.predict.CSM.X.rpart.prob 13.73439
## sold.fctr.predict.CSM.X.glmnet.prob 0.00000
## Ensemble.repeatedcv.glmnet.importance
## sold.fctr.predict.CSM.X.gbm.prob 100.00000
## sold.fctr.predict.CSM.X.avNNet.prob 89.06837
## sold.fctr.predict.CSM.X.Interact.glmnet.prob 80.98388
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.prob 67.49716
## sold.fctr.predict.CSM.X.center.scale.bagEarth.prob 58.56381
## sold.fctr.predict.CSM.X.bagEarth.prob 58.31636
## sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.prob 52.29685
## sold.fctr.predict.CSM.X.nnet.prob 48.20355
## sold.fctr.predict.CSM.X.glm.prob 47.22561
## sold.fctr.predict.All.X.glmnet.prob 44.65453
## sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.prob 40.81531
## sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.prob 39.03268
## sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.prob 36.62686
## sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.prob 36.19640
## sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.prob 33.16895
## sold.fctr.predict.RFE.X.glmnet.prob 32.20199
## sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.prob 31.67832
## sold.fctr.predict.CSM.X.spatialSign.bagEarth.prob 31.12100
## sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.prob 30.90820
## sold.fctr.predict.RFE.X.glm.prob 30.56269
## sold.fctr.predict.Max.cor.Y.rpart.prob 25.06619
## sold.fctr.predict.Low.cor.X.glmnet.prob 24.79841
## sold.fctr.predict.CSM.X.bayesglm.prob 23.62630
## sold.fctr.predict.CSM.X.earth.prob 20.08059
## sold.fctr.predict.Interact.High.cor.Y.glmnet.prob 19.63196
## sold.fctr.predict.CSM.X.rpart.prob 13.73439
## sold.fctr.predict.CSM.X.glmnet.prob 0.00000# Used again in fit.data.training & predict.data.new chunks
glb_analytics_diag_plots <- function(obs_df, mdl_id, prob_threshold=NULL) {
if (!is.null(featsimp_df <- glb_featsimp_df)) {
featsimp_df$feat <- gsub("`(.*?)`", "\\1", row.names(featsimp_df))
featsimp_df$feat.interact <- gsub("(.*?):(.*)", "\\2", featsimp_df$feat)
featsimp_df$feat <- gsub("(.*?):(.*)", "\\1", featsimp_df$feat)
featsimp_df$feat.interact <- ifelse(featsimp_df$feat.interact == featsimp_df$feat,
NA, featsimp_df$feat.interact)
featsimp_df$feat <- gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat)
featsimp_df$feat.interact <- gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat.interact)
featsimp_df <- orderBy(~ -importance.max, summaryBy(importance ~ feat + feat.interact,
data=featsimp_df, FUN=max))
#rex_str=":(.*)"; txt_vctr=tail(featsimp_df$feat); ret_lst <- regexec(rex_str, txt_vctr); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
featsimp_df <- subset(featsimp_df, !is.na(importance.max))
if (nrow(featsimp_df) > 5) {
warning("Limiting important feature scatter plots to 5 out of ", nrow(featsimp_df))
featsimp_df <- head(featsimp_df, 5)
}
# if (!all(is.na(featsimp_df$feat.interact)))
# stop("not implemented yet")
rsp_var_out <- paste0(glb_rsp_var_out, mdl_id)
for (var in featsimp_df$feat) {
plot_df <- melt(obs_df, id.vars=var,
measure.vars=c(glb_rsp_var, rsp_var_out))
# if (var == "<feat_name>") print(myplot_scatter(plot_df, var, "value",
# facet_colcol_name="variable") +
# geom_vline(xintercept=<divider_val>, linetype="dotted")) else
print(myplot_scatter(plot_df, var, "value", colorcol_name="variable",
facet_colcol_name="variable", jitter=TRUE) +
guides(color=FALSE))
}
}
if (glb_is_regression) {
if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
warning("No important features in glb_fin_mdl") else
print(myplot_prediction_regression(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var, rsp_var_out=rsp_var_out,
id_vars=glb_id_var)
# + facet_wrap(reformulate(featsimp_df$feat[2])) # if [1 or 2] is a factor
# + geom_point(aes_string(color="<col_name>.fctr")) # to color the plot
)
}
if (glb_is_classification) {
if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
warning("No features in selected model are statistically important")
else print(myplot_prediction_classification(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var,
rsp_var_out=rsp_var_out,
id_vars=glb_id_var,
prob_threshold=prob_threshold)
# + geom_hline(yintercept=<divider_val>, linetype = "dotted")
)
}
}
if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df = glb_OOBobs_df, mdl_id = glb_sel_mdl_id,
prob_threshold = glb_models_df[glb_models_df$id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id) 
## [1] "Min/Max Boundaries: "
## UniqueID sold.fctr sold.fctr.predict.CSM.X.center.scale.bagEarth.prob
## 1849 11860 Y 0.1477262
## 1002 11005 Y 0.5954188
## 4 10004 N 0.1764333
## sold.fctr.predict.CSM.X.center.scale.bagEarth
## 1849 N
## 1002 Y
## 4 N
## sold.fctr.predict.CSM.X.center.scale.bagEarth.err
## 1849 TRUE
## 1002 FALSE
## 4 FALSE
## sold.fctr.predict.CSM.X.center.scale.bagEarth.err.abs
## 1849 0.8522738
## 1002 0.4045812
## 4 0.1764333
## sold.fctr.predict.CSM.X.center.scale.bagEarth.accurate
## 1849 FALSE
## 1002 TRUE
## 4 TRUE
## sold.fctr.predict.CSM.X.bagEarth.prob
## 1849 0.1329867
## 1002 0.6291988
## 4 0.1915161
## sold.fctr.predict.CSM.X.bagEarth sold.fctr.predict.CSM.X.bagEarth.err
## 1849 N TRUE
## 1002 Y FALSE
## 4 N FALSE
## sold.fctr.predict.CSM.X.bagEarth.err.abs
## 1849 0.8670133
## 1002 0.3708012
## 4 0.1915161
## sold.fctr.predict.CSM.X.bagEarth.accurate
## 1849 FALSE
## 1002 TRUE
## 4 TRUE
## sold.fctr.predict.CSM.X.Interact.glmnet.prob
## 1849 0.3381240
## 1002 0.3736570
## 4 0.1882217
## sold.fctr.predict.CSM.X.Interact.glmnet
## 1849 N
## 1002 N
## 4 N
## sold.fctr.predict.CSM.X.Interact.glmnet.err
## 1849 TRUE
## 1002 TRUE
## 4 FALSE
## sold.fctr.predict.CSM.X.Interact.glmnet.err.abs
## 1849 0.6618760
## 1002 0.6263430
## 4 0.1882217
## sold.fctr.predict.CSM.X.Interact.glmnet.accurate
## 1849 FALSE
## 1002 FALSE
## 4 TRUE
## sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.prob
## 1849 0.1413893
## 1002 0.6370625
## 4 0.2035586
## sold.fctr.predict.CSM.X.YeoJohnson.bagEarth
## 1849 N
## 1002 Y
## 4 N
## sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.err
## 1849 TRUE
## 1002 FALSE
## 4 FALSE
## sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.err.abs
## 1849 0.8586107
## 1002 0.3629375
## 4 0.2035586
## sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.accurate
## 1849 FALSE
## 1002 TRUE
## 4 TRUE
## sold.fctr.predict.CSM.X.earth.prob sold.fctr.predict.CSM.X.earth
## 1849 0.46511628 Y
## 1002 0.96721311 Y
## 4 0.09882353 N
## sold.fctr.predict.CSM.X.earth.err
## 1849 FALSE
## 1002 FALSE
## 4 FALSE
## sold.fctr.predict.CSM.X.earth.err.abs
## 1849 0.53488372
## 1002 0.03278689
## 4 0.09882353
## sold.fctr.predict.CSM.X.earth.accurate
## 1849 TRUE
## 1002 TRUE
## 4 TRUE
## sold.fctr.predict.CSM.X.spatialSign.bagEarth.prob
## 1849 0.46555547
## 1002 0.48319997
## 4 0.09174694
## sold.fctr.predict.CSM.X.spatialSign.bagEarth
## 1849 Y
## 1002 Y
## 4 N
## sold.fctr.predict.CSM.X.spatialSign.bagEarth.err
## 1849 FALSE
## 1002 FALSE
## 4 FALSE
## sold.fctr.predict.CSM.X.spatialSign.bagEarth.err.abs
## 1849 0.53444453
## 1002 0.51680003
## 4 0.09174694
## sold.fctr.predict.CSM.X.spatialSign.bagEarth.accurate
## 1849 TRUE
## 1002 TRUE
## 4 TRUE
## sold.fctr.predict.CSM.X.nnet.prob sold.fctr.predict.CSM.X.nnet
## 1849 0.2664095 N
## 1002 0.5602993 Y
## 4 0.6497008 Y
## sold.fctr.predict.CSM.X.nnet.err sold.fctr.predict.CSM.X.nnet.err.abs
## 1849 TRUE 0.7335905
## 1002 FALSE 0.4397007
## 4 TRUE 0.6497008
## sold.fctr.predict.CSM.X.nnet.accurate
## 1849 FALSE
## 1002 TRUE
## 4 FALSE
## sold.fctr.predict.CSM.X.avNNet.prob sold.fctr.predict.CSM.X.avNNet
## 1849 0.1386709 N
## 1002 0.2480634 N
## 4 0.3311557 Y
## sold.fctr.predict.CSM.X.avNNet.err
## 1849 TRUE
## 1002 TRUE
## 4 TRUE
## sold.fctr.predict.CSM.X.avNNet.err.abs
## 1849 0.8613291
## 1002 0.7519366
## 4 0.3311557
## sold.fctr.predict.CSM.X.avNNet.accurate
## 1849 FALSE
## 1002 FALSE
## 4 FALSE
## sold.fctr.predict.RFE.X.glm.prob sold.fctr.predict.RFE.X.glm
## 1849 0.2866853 N
## 1002 0.6320015 Y
## 4 0.4572277 N
## sold.fctr.predict.RFE.X.glm.err sold.fctr.predict.RFE.X.glm.err.abs
## 1849 TRUE 0.7133147
## 1002 FALSE 0.3679985
## 4 FALSE 0.4572277
## sold.fctr.predict.RFE.X.glm.accurate sold.fctr.predict.CSM.X.glm.prob
## 1849 FALSE 0.3226793
## 1002 TRUE 0.5173889
## 4 TRUE 0.4834207
## sold.fctr.predict.CSM.X.glm sold.fctr.predict.CSM.X.glm.err
## 1849 N TRUE
## 1002 Y FALSE
## 4 N FALSE
## sold.fctr.predict.CSM.X.glm.err.abs
## 1849 0.6773207
## 1002 0.4826111
## 4 0.4834207
## sold.fctr.predict.CSM.X.glm.accurate
## 1849 FALSE
## 1002 TRUE
## 4 TRUE
## sold.fctr.predict.CSM.X.glmnet.prob sold.fctr.predict.CSM.X.glmnet
## 1849 0.2839467 N
## 1002 0.5445169 Y
## 4 0.4141586 Y
## sold.fctr.predict.CSM.X.glmnet.err
## 1849 TRUE
## 1002 FALSE
## 4 TRUE
## sold.fctr.predict.CSM.X.glmnet.err.abs
## 1849 0.7160533
## 1002 0.4554831
## 4 0.4141586
## sold.fctr.predict.CSM.X.glmnet.accurate
## 1849 FALSE
## 1002 TRUE
## 4 FALSE
## sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.prob
## 1849 0.3873465
## 1002 0.2560666
## 4 0.1991893
## sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet
## 1849 N
## 1002 N
## 4 N
## sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.err
## 1849 TRUE
## 1002 TRUE
## 4 FALSE
## sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.err.abs
## 1849 0.6126535
## 1002 0.7439334
## 4 0.1991893
## sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.accurate
## 1849 FALSE
## 1002 FALSE
## 4 TRUE
## sold.fctr.predict.CSM.X.gbm.prob sold.fctr.predict.CSM.X.gbm
## 1849 0.1434620 N
## 1002 0.3501574 Y
## 4 0.1636142 N
## sold.fctr.predict.CSM.X.gbm.err sold.fctr.predict.CSM.X.gbm.err.abs
## 1849 TRUE 0.8565380
## 1002 FALSE 0.6498426
## 4 FALSE 0.1636142
## sold.fctr.predict.CSM.X.gbm.accurate
## 1849 FALSE
## 1002 TRUE
## 4 TRUE
## sold.fctr.predict.Interact.High.cor.Y.glmnet.prob
## 1849 0.3026544
## 1002 0.2358328
## 4 0.2045922
## sold.fctr.predict.Interact.High.cor.Y.glmnet
## 1849 Y
## 1002 N
## 4 N
## sold.fctr.predict.Interact.High.cor.Y.glmnet.err
## 1849 FALSE
## 1002 TRUE
## 4 FALSE
## sold.fctr.predict.Interact.High.cor.Y.glmnet.err.abs
## 1849 0.6973456
## 1002 0.7641672
## 4 0.2045922
## sold.fctr.predict.Interact.High.cor.Y.glmnet.accurate
## 1849 TRUE
## 1002 FALSE
## 4 TRUE
## sold.fctr.predict.Max.cor.Y.rpart.prob
## 1849 0.198556
## 1002 0.198556
## 4 0.198556
## sold.fctr.predict.Max.cor.Y.rpart
## 1849 N
## 1002 N
## 4 N
## sold.fctr.predict.Max.cor.Y.rpart.err
## 1849 TRUE
## 1002 TRUE
## 4 FALSE
## sold.fctr.predict.Max.cor.Y.rpart.err.abs
## 1849 0.801444
## 1002 0.801444
## 4 0.198556
## sold.fctr.predict.Max.cor.Y.rpart.accurate
## 1849 FALSE
## 1002 FALSE
## 4 TRUE
## sold.fctr.predict.Low.cor.X.glmnet.prob
## 1849 0.2307892
## 1002 0.7355577
## 4 0.4506691
## sold.fctr.predict.Low.cor.X.glmnet
## 1849 N
## 1002 Y
## 4 Y
## sold.fctr.predict.Low.cor.X.glmnet.err
## 1849 TRUE
## 1002 FALSE
## 4 TRUE
## sold.fctr.predict.Low.cor.X.glmnet.err.abs
## 1849 0.7692108
## 1002 0.2644423
## 4 0.4506691
## sold.fctr.predict.Low.cor.X.glmnet.accurate
## 1849 FALSE
## 1002 TRUE
## 4 FALSE
## sold.fctr.predict.CSM.X.bayesglm.prob
## 1849 0.3114657
## 1002 0.6052644
## 4 0.5020357
## sold.fctr.predict.CSM.X.bayesglm sold.fctr.predict.CSM.X.bayesglm.err
## 1849 Y FALSE
## 1002 Y FALSE
## 4 Y TRUE
## sold.fctr.predict.CSM.X.bayesglm.err.abs
## 1849 0.6885343
## 1002 0.3947356
## 4 0.5020357
## sold.fctr.predict.CSM.X.bayesglm.accurate
## 1849 TRUE
## 1002 TRUE
## 4 FALSE
## sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.prob
## 1849 0.3982544
## 1002 0.3250985
## 4 0.2890148
## sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet
## 1849 N
## 1002 N
## 4 N
## sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.err
## 1849 TRUE
## 1002 TRUE
## 4 FALSE
## sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.err.abs
## 1849 0.6017456
## 1002 0.6749015
## 4 0.2890148
## sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.accurate
## 1849 FALSE
## 1002 FALSE
## 4 TRUE
## sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.prob
## 1849 0.3971510
## 1002 0.3191060
## 4 0.2809366
## sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet
## 1849 N
## 1002 N
## 4 N
## sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.err
## 1849 TRUE
## 1002 TRUE
## 4 FALSE
## sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.err.abs
## 1849 0.6028490
## 1002 0.6808940
## 4 0.2809366
## sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.accurate
## 1849 FALSE
## 1002 FALSE
## 4 TRUE
## sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.prob
## 1849 0.3990531
## 1002 0.3317237
## 4 0.2982040
## sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet
## 1849 N
## 1002 N
## 4 N
## sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.err
## 1849 TRUE
## 1002 TRUE
## 4 FALSE
## sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.err.abs
## 1849 0.6009469
## 1002 0.6682763
## 4 0.2982040
## sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.accurate
## 1849 FALSE
## 1002 FALSE
## 4 TRUE
## sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.prob
## 1849 0.3992815
## 1002 0.3390339
## 4 0.3087318
## sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet
## 1849 N
## 1002 N
## 4 N
## sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.err
## 1849 TRUE
## 1002 TRUE
## 4 FALSE
## sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.err.abs
## 1849 0.6007185
## 1002 0.6609661
## 4 0.3087318
## sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.accurate
## 1849 FALSE
## 1002 FALSE
## 4 TRUE
## sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.prob
## 1849 0.3992815
## 1002 0.3390339
## 4 0.3087318
## sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet
## 1849 N
## 1002 N
## 4 N
## sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.err
## 1849 TRUE
## 1002 TRUE
## 4 FALSE
## sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.err.abs
## 1849 0.6007185
## 1002 0.6609661
## 4 0.3087318
## sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.accurate
## 1849 FALSE
## 1002 FALSE
## 4 TRUE
## sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.prob
## 1849 0.3981215
## 1002 0.3466623
## 4 0.3204901
## sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet
## 1849 N
## 1002 N
## 4 N
## sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.err
## 1849 TRUE
## 1002 TRUE
## 4 FALSE
## sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.err.abs
## 1849 0.6018785
## 1002 0.6533377
## 4 0.3204901
## sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.accurate
## 1849 FALSE
## 1002 FALSE
## 4 TRUE
## sold.fctr.predict.RFE.X.glmnet.prob sold.fctr.predict.RFE.X.glmnet
## 1849 0.2652680 N
## 1002 0.6681064 Y
## 4 0.4871668 Y
## sold.fctr.predict.RFE.X.glmnet.err
## 1849 TRUE
## 1002 FALSE
## 4 TRUE
## sold.fctr.predict.RFE.X.glmnet.err.abs
## 1849 0.7347320
## 1002 0.3318936
## 4 0.4871668
## sold.fctr.predict.RFE.X.glmnet.accurate
## 1849 FALSE
## 1002 TRUE
## 4 FALSE
## sold.fctr.predict.All.X.glmnet.prob sold.fctr.predict.All.X.glmnet
## 1849 0.2701068 N
## 1002 0.6963844 Y
## 4 0.5208855 Y
## sold.fctr.predict.All.X.glmnet.err
## 1849 TRUE
## 1002 FALSE
## 4 TRUE
## sold.fctr.predict.All.X.glmnet.err.abs
## 1849 0.7298932
## 1002 0.3036156
## 4 0.5208855
## sold.fctr.predict.All.X.glmnet.accurate
## 1849 FALSE
## 1002 TRUE
## 4 FALSE
## sold.fctr.predict.CSM.X.rpart.prob sold.fctr.predict.CSM.X.rpart
## 1849 0.1971067 N
## 1002 0.1971067 N
## 4 0.1971067 N
## sold.fctr.predict.CSM.X.rpart.err
## 1849 TRUE
## 1002 TRUE
## 4 FALSE
## sold.fctr.predict.CSM.X.rpart.err.abs
## 1849 0.8028933
## 1002 0.8028933
## 4 0.1971067
## sold.fctr.predict.CSM.X.rpart.accurate
## 1849 FALSE
## 1002 FALSE
## 4 TRUE
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.prob
## 1849 0.1724138
## 1002 0.1000000
## 4 0.1939163
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart
## 1849 N
## 1002 N
## 4 N
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.err
## 1849 TRUE
## 1002 TRUE
## 4 FALSE
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.err.abs
## 1849 0.8275862
## 1002 0.9000000
## 4 0.1939163
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.accurate
## 1849 FALSE
## 1002 FALSE
## 4 TRUE
## sold.fctr.predict.Ensemble.repeatedcv.glmnet.prob
## 1849 0.009319961
## 1002 0.052494567
## 4 0.048482199
## sold.fctr.predict.Ensemble.repeatedcv.glmnet
## 1849 N
## 1002 N
## 4 N
## sold.fctr.predict.Ensemble.repeatedcv.glmnet.err
## 1849 TRUE
## 1002 TRUE
## 4 FALSE
## sold.fctr.predict.Ensemble.repeatedcv.glmnet.err.abs
## 1849 0.9906800
## 1002 0.9475054
## 4 0.0484822
## sold.fctr.predict.Ensemble.repeatedcv.glmnet.accurate
## 1849 FALSE
## 1002 FALSE
## 4 TRUE
## sold.fctr.predict.Ensemble.repeatedcv.glmnet.error .label
## 1849 -0.1906800 11860
## 1002 -0.1475054 11005
## 4 0.0000000 10004
## [1] "Inaccurate: "
## UniqueID sold.fctr sold.fctr.predict.CSM.X.center.scale.bagEarth.prob
## 1849 11860 Y 0.14772616
## 688 10689 Y 0.06767368
## 692 10693 Y 0.06840598
## 1469 11476 Y 0.46947664
## 205 10205 Y 0.15510421
## 843 10846 Y 0.05557578
## sold.fctr.predict.CSM.X.center.scale.bagEarth
## 1849 N
## 688 N
## 692 N
## 1469 N
## 205 N
## 843 N
## sold.fctr.predict.CSM.X.center.scale.bagEarth.err
## 1849 TRUE
## 688 TRUE
## 692 TRUE
## 1469 TRUE
## 205 TRUE
## 843 TRUE
## sold.fctr.predict.CSM.X.center.scale.bagEarth.err.abs
## 1849 0.8522738
## 688 0.9323263
## 692 0.9315940
## 1469 0.5305234
## 205 0.8448958
## 843 0.9444242
## sold.fctr.predict.CSM.X.center.scale.bagEarth.accurate
## 1849 FALSE
## 688 FALSE
## 692 FALSE
## 1469 FALSE
## 205 FALSE
## 843 FALSE
## sold.fctr.predict.CSM.X.bagEarth.prob
## 1849 0.13298665
## 688 0.07600093
## 692 0.08594731
## 1469 0.43942761
## 205 0.19234046
## 843 0.05716693
## sold.fctr.predict.CSM.X.bagEarth sold.fctr.predict.CSM.X.bagEarth.err
## 1849 N TRUE
## 688 N TRUE
## 692 N TRUE
## 1469 N TRUE
## 205 N TRUE
## 843 N TRUE
## sold.fctr.predict.CSM.X.bagEarth.err.abs
## 1849 0.8670133
## 688 0.9239991
## 692 0.9140527
## 1469 0.5605724
## 205 0.8076595
## 843 0.9428331
## sold.fctr.predict.CSM.X.bagEarth.accurate
## 1849 FALSE
## 688 FALSE
## 692 FALSE
## 1469 FALSE
## 205 FALSE
## 843 FALSE
## sold.fctr.predict.CSM.X.Interact.glmnet.prob
## 1849 0.3381240
## 688 0.1940504
## 692 0.2262072
## 1469 0.4772616
## 205 0.1504025
## 843 0.1383442
## sold.fctr.predict.CSM.X.Interact.glmnet
## 1849 N
## 688 N
## 692 N
## 1469 Y
## 205 N
## 843 N
## sold.fctr.predict.CSM.X.Interact.glmnet.err
## 1849 TRUE
## 688 TRUE
## 692 TRUE
## 1469 FALSE
## 205 TRUE
## 843 TRUE
## sold.fctr.predict.CSM.X.Interact.glmnet.err.abs
## 1849 0.6618760
## 688 0.8059496
## 692 0.7737928
## 1469 0.5227384
## 205 0.8495975
## 843 0.8616558
## sold.fctr.predict.CSM.X.Interact.glmnet.accurate
## 1849 FALSE
## 688 FALSE
## 692 FALSE
## 1469 TRUE
## 205 FALSE
## 843 FALSE
## sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.prob
## 1849 0.14138933
## 688 0.07366329
## 692 0.07739090
## 1469 0.41765695
## 205 0.12274569
## 843 0.06597323
## sold.fctr.predict.CSM.X.YeoJohnson.bagEarth
## 1849 N
## 688 N
## 692 N
## 1469 Y
## 205 N
## 843 N
## sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.err
## 1849 TRUE
## 688 TRUE
## 692 TRUE
## 1469 FALSE
## 205 TRUE
## 843 TRUE
## sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.err.abs
## 1849 0.8586107
## 688 0.9263367
## 692 0.9226091
## 1469 0.5823431
## 205 0.8772543
## 843 0.9340268
## sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.accurate
## 1849 FALSE
## 688 FALSE
## 692 FALSE
## 1469 TRUE
## 205 FALSE
## 843 FALSE
## sold.fctr.predict.CSM.X.earth.prob sold.fctr.predict.CSM.X.earth
## 1849 0.46511628 Y
## 688 0.09882353 N
## 692 0.09882353 N
## 1469 0.65686275 Y
## 205 0.09882353 N
## 843 0.09882353 N
## sold.fctr.predict.CSM.X.earth.err
## 1849 FALSE
## 688 TRUE
## 692 TRUE
## 1469 FALSE
## 205 TRUE
## 843 TRUE
## sold.fctr.predict.CSM.X.earth.err.abs
## 1849 0.5348837
## 688 0.9011765
## 692 0.9011765
## 1469 0.3431373
## 205 0.9011765
## 843 0.9011765
## sold.fctr.predict.CSM.X.earth.accurate
## 1849 TRUE
## 688 FALSE
## 692 FALSE
## 1469 TRUE
## 205 FALSE
## 843 FALSE
## sold.fctr.predict.CSM.X.spatialSign.bagEarth.prob
## 1849 0.46555547
## 688 0.07993072
## 692 0.11545744
## 1469 0.51720866
## 205 0.11679761
## 843 0.07024188
## sold.fctr.predict.CSM.X.spatialSign.bagEarth
## 1849 Y
## 688 N
## 692 N
## 1469 Y
## 205 N
## 843 N
## sold.fctr.predict.CSM.X.spatialSign.bagEarth.err
## 1849 FALSE
## 688 TRUE
## 692 TRUE
## 1469 FALSE
## 205 TRUE
## 843 TRUE
## sold.fctr.predict.CSM.X.spatialSign.bagEarth.err.abs
## 1849 0.5344445
## 688 0.9200693
## 692 0.8845426
## 1469 0.4827913
## 205 0.8832024
## 843 0.9297581
## sold.fctr.predict.CSM.X.spatialSign.bagEarth.accurate
## 1849 TRUE
## 688 FALSE
## 692 FALSE
## 1469 TRUE
## 205 FALSE
## 843 FALSE
## sold.fctr.predict.CSM.X.nnet.prob sold.fctr.predict.CSM.X.nnet
## 1849 0.26640948 N
## 688 0.19879872 N
## 692 0.23310739 N
## 1469 0.19970021 N
## 205 0.15126193 N
## 843 0.05024294 N
## sold.fctr.predict.CSM.X.nnet.err sold.fctr.predict.CSM.X.nnet.err.abs
## 1849 TRUE 0.7335905
## 688 TRUE 0.8012013
## 692 TRUE 0.7668926
## 1469 TRUE 0.8002998
## 205 TRUE 0.8487381
## 843 TRUE 0.9497571
## sold.fctr.predict.CSM.X.nnet.accurate
## 1849 FALSE
## 688 FALSE
## 692 FALSE
## 1469 FALSE
## 205 FALSE
## 843 FALSE
## sold.fctr.predict.CSM.X.avNNet.prob sold.fctr.predict.CSM.X.avNNet
## 1849 0.1386709 N
## 688 0.1206474 N
## 692 0.1199940 N
## 1469 0.1220019 N
## 205 0.1466390 N
## 843 0.1203155 N
## sold.fctr.predict.CSM.X.avNNet.err
## 1849 TRUE
## 688 TRUE
## 692 TRUE
## 1469 TRUE
## 205 TRUE
## 843 TRUE
## sold.fctr.predict.CSM.X.avNNet.err.abs
## 1849 0.8613291
## 688 0.8793526
## 692 0.8800060
## 1469 0.8779981
## 205 0.8533610
## 843 0.8796845
## sold.fctr.predict.CSM.X.avNNet.accurate
## 1849 FALSE
## 688 FALSE
## 692 FALSE
## 1469 FALSE
## 205 FALSE
## 843 FALSE
## sold.fctr.predict.RFE.X.glm.prob sold.fctr.predict.RFE.X.glm
## 1849 0.28668534 N
## 688 0.14811238 N
## 692 0.25950136 N
## 1469 0.29518812 N
## 205 0.21189622 N
## 843 0.03706248 N
## sold.fctr.predict.RFE.X.glm.err sold.fctr.predict.RFE.X.glm.err.abs
## 1849 TRUE 0.7133147
## 688 TRUE 0.8518876
## 692 TRUE 0.7404986
## 1469 TRUE 0.7048119
## 205 TRUE 0.7881038
## 843 TRUE 0.9629375
## sold.fctr.predict.RFE.X.glm.accurate sold.fctr.predict.CSM.X.glm.prob
## 1849 FALSE 0.32267926
## 688 FALSE 0.16807376
## 692 FALSE 0.31171945
## 1469 FALSE 0.23756413
## 205 FALSE 0.19422353
## 843 FALSE 0.03981926
## sold.fctr.predict.CSM.X.glm sold.fctr.predict.CSM.X.glm.err
## 1849 N TRUE
## 688 N TRUE
## 692 N TRUE
## 1469 N TRUE
## 205 N TRUE
## 843 N TRUE
## sold.fctr.predict.CSM.X.glm.err.abs
## 1849 0.6773207
## 688 0.8319262
## 692 0.6882806
## 1469 0.7624359
## 205 0.8057765
## 843 0.9601807
## sold.fctr.predict.CSM.X.glm.accurate
## 1849 FALSE
## 688 FALSE
## 692 FALSE
## 1469 FALSE
## 205 FALSE
## 843 FALSE
## sold.fctr.predict.CSM.X.glmnet.prob sold.fctr.predict.CSM.X.glmnet
## 1849 0.28394675 N
## 688 0.19180195 N
## 692 0.17387655 N
## 1469 0.34323501 N
## 205 0.19512378 N
## 843 0.05331325 N
## sold.fctr.predict.CSM.X.glmnet.err
## 1849 TRUE
## 688 TRUE
## 692 TRUE
## 1469 TRUE
## 205 TRUE
## 843 TRUE
## sold.fctr.predict.CSM.X.glmnet.err.abs
## 1849 0.7160533
## 688 0.8081981
## 692 0.8261235
## 1469 0.6567650
## 205 0.8048762
## 843 0.9466868
## sold.fctr.predict.CSM.X.glmnet.accurate
## 1849 FALSE
## 688 FALSE
## 692 FALSE
## 1469 FALSE
## 205 FALSE
## 843 FALSE
## sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.prob
## 1849 0.3873465
## 688 0.2308607
## 692 0.1798914
## 1469 0.7179368
## 205 0.1007294
## 843 0.1061850
## sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet
## 1849 N
## 688 N
## 692 N
## 1469 Y
## 205 N
## 843 N
## sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.err
## 1849 TRUE
## 688 TRUE
## 692 TRUE
## 1469 FALSE
## 205 TRUE
## 843 TRUE
## sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.err.abs
## 1849 0.6126535
## 688 0.7691393
## 692 0.8201086
## 1469 0.2820632
## 205 0.8992706
## 843 0.8938150
## sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.accurate
## 1849 FALSE
## 688 FALSE
## 692 FALSE
## 1469 TRUE
## 205 FALSE
## 843 FALSE
## sold.fctr.predict.CSM.X.gbm.prob sold.fctr.predict.CSM.X.gbm
## 1849 0.14346204 N
## 688 0.07829822 N
## 692 0.02386776 N
## 1469 0.27144106 N
## 205 0.08322572 N
## 843 0.03738122 N
## sold.fctr.predict.CSM.X.gbm.err sold.fctr.predict.CSM.X.gbm.err.abs
## 1849 TRUE 0.8565380
## 688 TRUE 0.9217018
## 692 TRUE 0.9761322
## 1469 TRUE 0.7285589
## 205 TRUE 0.9167743
## 843 TRUE 0.9626188
## sold.fctr.predict.CSM.X.gbm.accurate
## 1849 FALSE
## 688 FALSE
## 692 FALSE
## 1469 FALSE
## 205 FALSE
## 843 FALSE
## sold.fctr.predict.Interact.High.cor.Y.glmnet.prob
## 1849 0.3026544
## 688 0.2222748
## 692 0.1933584
## 1469 0.7492495
## 205 0.1412175
## 843 0.1452660
## sold.fctr.predict.Interact.High.cor.Y.glmnet
## 1849 Y
## 688 N
## 692 N
## 1469 Y
## 205 N
## 843 N
## sold.fctr.predict.Interact.High.cor.Y.glmnet.err
## 1849 FALSE
## 688 TRUE
## 692 TRUE
## 1469 FALSE
## 205 TRUE
## 843 TRUE
## sold.fctr.predict.Interact.High.cor.Y.glmnet.err.abs
## 1849 0.6973456
## 688 0.7777252
## 692 0.8066416
## 1469 0.2507505
## 205 0.8587825
## 843 0.8547340
## sold.fctr.predict.Interact.High.cor.Y.glmnet.accurate
## 1849 TRUE
## 688 FALSE
## 692 FALSE
## 1469 TRUE
## 205 FALSE
## 843 FALSE
## sold.fctr.predict.Max.cor.Y.rpart.prob
## 1849 0.1985560
## 688 0.1985560
## 692 0.1985560
## 1469 0.5862069
## 205 0.1985560
## 843 0.1985560
## sold.fctr.predict.Max.cor.Y.rpart
## 1849 N
## 688 N
## 692 N
## 1469 Y
## 205 N
## 843 N
## sold.fctr.predict.Max.cor.Y.rpart.err
## 1849 TRUE
## 688 TRUE
## 692 TRUE
## 1469 FALSE
## 205 TRUE
## 843 TRUE
## sold.fctr.predict.Max.cor.Y.rpart.err.abs
## 1849 0.8014440
## 688 0.8014440
## 692 0.8014440
## 1469 0.4137931
## 205 0.8014440
## 843 0.8014440
## sold.fctr.predict.Max.cor.Y.rpart.accurate
## 1849 FALSE
## 688 FALSE
## 692 FALSE
## 1469 TRUE
## 205 FALSE
## 843 FALSE
## sold.fctr.predict.Low.cor.X.glmnet.prob
## 1849 0.23078921
## 688 0.18218617
## 692 0.07213544
## 1469 0.36059287
## 205 0.21760720
## 843 0.04647931
## sold.fctr.predict.Low.cor.X.glmnet
## 1849 N
## 688 N
## 692 N
## 1469 Y
## 205 N
## 843 N
## sold.fctr.predict.Low.cor.X.glmnet.err
## 1849 TRUE
## 688 TRUE
## 692 TRUE
## 1469 FALSE
## 205 TRUE
## 843 TRUE
## sold.fctr.predict.Low.cor.X.glmnet.err.abs
## 1849 0.7692108
## 688 0.8178138
## 692 0.9278646
## 1469 0.6394071
## 205 0.7823928
## 843 0.9535207
## sold.fctr.predict.Low.cor.X.glmnet.accurate
## 1849 FALSE
## 688 FALSE
## 692 FALSE
## 1469 TRUE
## 205 FALSE
## 843 FALSE
## sold.fctr.predict.CSM.X.bayesglm.prob
## 1849 0.31146575
## 688 0.21056168
## 692 0.18368340
## 1469 0.30101733
## 205 0.20968930
## 843 0.04938119
## sold.fctr.predict.CSM.X.bayesglm sold.fctr.predict.CSM.X.bayesglm.err
## 1849 Y FALSE
## 688 N TRUE
## 692 N TRUE
## 1469 Y FALSE
## 205 N TRUE
## 843 N TRUE
## sold.fctr.predict.CSM.X.bayesglm.err.abs
## 1849 0.6885343
## 688 0.7894383
## 692 0.8163166
## 1469 0.6989827
## 205 0.7903107
## 843 0.9506188
## sold.fctr.predict.CSM.X.bayesglm.accurate
## 1849 TRUE
## 688 FALSE
## 692 FALSE
## 1469 TRUE
## 205 FALSE
## 843 FALSE
## sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.prob
## 1849 0.3982544
## 688 0.3095998
## 692 0.2757067
## 1469 0.6287648
## 205 0.2113164
## 843 0.2164848
## sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet
## 1849 N
## 688 N
## 692 N
## 1469 Y
## 205 N
## 843 N
## sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.err
## 1849 TRUE
## 688 TRUE
## 692 TRUE
## 1469 FALSE
## 205 TRUE
## 843 TRUE
## sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.err.abs
## 1849 0.6017456
## 688 0.6904002
## 692 0.7242933
## 1469 0.3712352
## 205 0.7886836
## 843 0.7835152
## sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.accurate
## 1849 FALSE
## 688 FALSE
## 692 FALSE
## 1469 TRUE
## 205 FALSE
## 843 FALSE
## sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.prob
## 1849 0.3971510
## 688 0.3026776
## 692 0.2669353
## 1469 0.6361690
## 205 0.1999477
## 843 0.2052706
## sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet
## 1849 N
## 688 N
## 692 N
## 1469 Y
## 205 N
## 843 N
## sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.err
## 1849 TRUE
## 688 TRUE
## 692 TRUE
## 1469 FALSE
## 205 TRUE
## 843 TRUE
## sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.err.abs
## 1849 0.6028490
## 688 0.6973224
## 692 0.7330647
## 1469 0.3638310
## 205 0.8000523
## 843 0.7947294
## sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.accurate
## 1849 FALSE
## 688 FALSE
## 692 FALSE
## 1469 TRUE
## 205 FALSE
## 843 FALSE
## sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.prob
## 1849 0.3990531
## 688 0.3173588
## 692 0.2857679
## 1469 0.6206199
## 205 0.2248440
## 843 0.2297890
## sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet
## 1849 N
## 688 N
## 692 N
## 1469 Y
## 205 N
## 843 N
## sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.err
## 1849 TRUE
## 688 TRUE
## 692 TRUE
## 1469 FALSE
## 205 TRUE
## 843 TRUE
## sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.err.abs
## 1849 0.6009469
## 688 0.6826412
## 692 0.7142321
## 1469 0.3793801
## 205 0.7751560
## 843 0.7702110
## sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.accurate
## 1849 FALSE
## 688 FALSE
## 692 FALSE
## 1469 TRUE
## 205 FALSE
## 843 FALSE
## sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.prob
## 1849 0.3992815
## 688 0.3260802
## 692 0.2974158
## 1469 0.6116721
## 205 0.2412084
## 843 0.2458272
## sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet
## 1849 N
## 688 N
## 692 N
## 1469 Y
## 205 N
## 843 N
## sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.err
## 1849 TRUE
## 688 TRUE
## 692 TRUE
## 1469 FALSE
## 205 TRUE
## 843 TRUE
## sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.err.abs
## 1849 0.6007185
## 688 0.6739198
## 692 0.7025842
## 1469 0.3883279
## 205 0.7587916
## 843 0.7541728
## sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.accurate
## 1849 FALSE
## 688 FALSE
## 692 FALSE
## 1469 TRUE
## 205 FALSE
## 843 FALSE
## sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.prob
## 1849 0.3992815
## 688 0.3260802
## 692 0.2974158
## 1469 0.6116721
## 205 0.2412084
## 843 0.2458272
## sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet
## 1849 N
## 688 N
## 692 N
## 1469 Y
## 205 N
## 843 N
## sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.err
## 1849 TRUE
## 688 TRUE
## 692 TRUE
## 1469 FALSE
## 205 TRUE
## 843 TRUE
## sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.err.abs
## 1849 0.6007185
## 688 0.6739198
## 692 0.7025842
## 1469 0.3883279
## 205 0.7587916
## 843 0.7541728
## sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.accurate
## 1849 FALSE
## 688 FALSE
## 692 FALSE
## 1469 TRUE
## 205 FALSE
## 843 FALSE
## sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.prob
## 1849 0.3981215
## 688 0.3355045
## 692 0.3106465
## 1469 0.6023569
## 205 0.2610038
## 843 0.2651387
## sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet
## 1849 N
## 688 N
## 692 N
## 1469 Y
## 205 N
## 843 N
## sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.err
## 1849 TRUE
## 688 TRUE
## 692 TRUE
## 1469 FALSE
## 205 TRUE
## 843 TRUE
## sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.err.abs
## 1849 0.6018785
## 688 0.6644955
## 692 0.6893535
## 1469 0.3976431
## 205 0.7389962
## 843 0.7348613
## sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.accurate
## 1849 FALSE
## 688 FALSE
## 692 FALSE
## 1469 TRUE
## 205 FALSE
## 843 FALSE
## sold.fctr.predict.RFE.X.glmnet.prob sold.fctr.predict.RFE.X.glmnet
## 1849 0.26526803 N
## 688 0.19771073 N
## 692 0.15295923 N
## 1469 0.33665804 Y
## 205 0.23967984 N
## 843 0.04763596 N
## sold.fctr.predict.RFE.X.glmnet.err
## 1849 TRUE
## 688 TRUE
## 692 TRUE
## 1469 FALSE
## 205 TRUE
## 843 TRUE
## sold.fctr.predict.RFE.X.glmnet.err.abs
## 1849 0.7347320
## 688 0.8022893
## 692 0.8470408
## 1469 0.6633420
## 205 0.7603202
## 843 0.9523640
## sold.fctr.predict.RFE.X.glmnet.accurate
## 1849 FALSE
## 688 FALSE
## 692 FALSE
## 1469 TRUE
## 205 FALSE
## 843 FALSE
## sold.fctr.predict.All.X.glmnet.prob sold.fctr.predict.All.X.glmnet
## 1849 0.27010684 N
## 688 0.18899334 N
## 692 0.18131809 N
## 1469 0.30821462 Y
## 205 0.23202675 N
## 843 0.04455329 N
## sold.fctr.predict.All.X.glmnet.err
## 1849 TRUE
## 688 TRUE
## 692 TRUE
## 1469 FALSE
## 205 TRUE
## 843 TRUE
## sold.fctr.predict.All.X.glmnet.err.abs
## 1849 0.7298932
## 688 0.8110067
## 692 0.8186819
## 1469 0.6917854
## 205 0.7679733
## 843 0.9554467
## sold.fctr.predict.All.X.glmnet.accurate
## 1849 FALSE
## 688 FALSE
## 692 FALSE
## 1469 TRUE
## 205 FALSE
## 843 FALSE
## sold.fctr.predict.CSM.X.rpart.prob sold.fctr.predict.CSM.X.rpart
## 1849 0.1971067 N
## 688 0.1971067 N
## 692 0.1971067 N
## 1469 0.3695652 Y
## 205 0.1971067 N
## 843 0.1971067 N
## sold.fctr.predict.CSM.X.rpart.err
## 1849 TRUE
## 688 TRUE
## 692 TRUE
## 1469 FALSE
## 205 TRUE
## 843 TRUE
## sold.fctr.predict.CSM.X.rpart.err.abs
## 1849 0.8028933
## 688 0.8028933
## 692 0.8028933
## 1469 0.6304348
## 205 0.8028933
## 843 0.8028933
## sold.fctr.predict.CSM.X.rpart.accurate
## 1849 FALSE
## 688 FALSE
## 692 FALSE
## 1469 TRUE
## 205 FALSE
## 843 FALSE
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.prob
## 1849 0.17241379
## 688 0.19391635
## 692 0.19391635
## 1469 0.63265306
## 205 0.08870968
## 843 0.08870968
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart
## 1849 N
## 688 N
## 692 N
## 1469 Y
## 205 N
## 843 N
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.err
## 1849 TRUE
## 688 TRUE
## 692 TRUE
## 1469 FALSE
## 205 TRUE
## 843 TRUE
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.err.abs
## 1849 0.8275862
## 688 0.8060837
## 692 0.8060837
## 1469 0.3673469
## 205 0.9112903
## 843 0.9112903
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.accurate
## 1849 FALSE
## 688 FALSE
## 692 FALSE
## 1469 TRUE
## 205 FALSE
## 843 FALSE
## sold.fctr.predict.Ensemble.repeatedcv.glmnet.prob
## 1849 0.009319961
## 688 0.016484499
## 692 0.022921881
## 1469 0.024392109
## 205 0.024978986
## 843 0.025663241
## sold.fctr.predict.Ensemble.repeatedcv.glmnet
## 1849 N
## 688 N
## 692 N
## 1469 N
## 205 N
## 843 N
## sold.fctr.predict.Ensemble.repeatedcv.glmnet.err
## 1849 TRUE
## 688 TRUE
## 692 TRUE
## 1469 TRUE
## 205 TRUE
## 843 TRUE
## sold.fctr.predict.Ensemble.repeatedcv.glmnet.err.abs
## 1849 0.9906800
## 688 0.9835155
## 692 0.9770781
## 1469 0.9756079
## 205 0.9750210
## 843 0.9743368
## sold.fctr.predict.Ensemble.repeatedcv.glmnet.accurate
## 1849 FALSE
## 688 FALSE
## 692 FALSE
## 1469 FALSE
## 205 FALSE
## 843 FALSE
## sold.fctr.predict.Ensemble.repeatedcv.glmnet.error
## 1849 -0.1906800
## 688 -0.1835155
## 692 -0.1770781
## 1469 -0.1756079
## 205 -0.1750210
## 843 -0.1743368
## UniqueID sold.fctr sold.fctr.predict.CSM.X.center.scale.bagEarth.prob
## 692 10693 Y 0.06840598
## 183 10183 Y 0.29110179
## 368 10369 N 0.14372806
## 172 10172 N 0.65409008
## 488 10489 N 0.35025695
## 1613 11622 N 0.77120380
## sold.fctr.predict.CSM.X.center.scale.bagEarth
## 692 N
## 183 N
## 368 N
## 172 Y
## 488 N
## 1613 Y
## sold.fctr.predict.CSM.X.center.scale.bagEarth.err
## 692 TRUE
## 183 TRUE
## 368 FALSE
## 172 TRUE
## 488 FALSE
## 1613 TRUE
## sold.fctr.predict.CSM.X.center.scale.bagEarth.err.abs
## 692 0.9315940
## 183 0.7088982
## 368 0.1437281
## 172 0.6540901
## 488 0.3502569
## 1613 0.7712038
## sold.fctr.predict.CSM.X.center.scale.bagEarth.accurate
## 692 FALSE
## 183 FALSE
## 368 TRUE
## 172 FALSE
## 488 TRUE
## 1613 FALSE
## sold.fctr.predict.CSM.X.bagEarth.prob
## 692 0.08594731
## 183 0.25167491
## 368 0.14942936
## 172 0.68737074
## 488 0.36262104
## 1613 0.76573336
## sold.fctr.predict.CSM.X.bagEarth sold.fctr.predict.CSM.X.bagEarth.err
## 692 N TRUE
## 183 N TRUE
## 368 N FALSE
## 172 Y TRUE
## 488 N FALSE
## 1613 Y TRUE
## sold.fctr.predict.CSM.X.bagEarth.err.abs
## 692 0.9140527
## 183 0.7483251
## 368 0.1494294
## 172 0.6873707
## 488 0.3626210
## 1613 0.7657334
## sold.fctr.predict.CSM.X.bagEarth.accurate
## 692 FALSE
## 183 FALSE
## 368 TRUE
## 172 FALSE
## 488 TRUE
## 1613 FALSE
## sold.fctr.predict.CSM.X.Interact.glmnet.prob
## 692 0.2262072
## 183 0.1458888
## 368 0.1616262
## 172 0.3355546
## 488 0.5190936
## 1613 0.8024475
## sold.fctr.predict.CSM.X.Interact.glmnet
## 692 N
## 183 N
## 368 N
## 172 N
## 488 Y
## 1613 Y
## sold.fctr.predict.CSM.X.Interact.glmnet.err
## 692 TRUE
## 183 TRUE
## 368 FALSE
## 172 FALSE
## 488 TRUE
## 1613 TRUE
## sold.fctr.predict.CSM.X.Interact.glmnet.err.abs
## 692 0.7737928
## 183 0.8541112
## 368 0.1616262
## 172 0.3355546
## 488 0.5190936
## 1613 0.8024475
## sold.fctr.predict.CSM.X.Interact.glmnet.accurate
## 692 FALSE
## 183 FALSE
## 368 TRUE
## 172 TRUE
## 488 FALSE
## 1613 FALSE
## sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.prob
## 692 0.0773909
## 183 0.2935606
## 368 0.1607956
## 172 0.6607424
## 488 0.3985474
## 1613 0.7878054
## sold.fctr.predict.CSM.X.YeoJohnson.bagEarth
## 692 N
## 183 N
## 368 N
## 172 Y
## 488 N
## 1613 Y
## sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.err
## 692 TRUE
## 183 TRUE
## 368 FALSE
## 172 TRUE
## 488 FALSE
## 1613 TRUE
## sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.err.abs
## 692 0.9226091
## 183 0.7064394
## 368 0.1607956
## 172 0.6607424
## 488 0.3985474
## 1613 0.7878054
## sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.accurate
## 692 FALSE
## 183 FALSE
## 368 TRUE
## 172 FALSE
## 488 TRUE
## 1613 FALSE
## sold.fctr.predict.CSM.X.earth.prob sold.fctr.predict.CSM.X.earth
## 692 0.09882353 N
## 183 0.09882353 N
## 368 0.09882353 N
## 172 0.46511628 Y
## 488 0.65686275 Y
## 1613 0.65686275 Y
## sold.fctr.predict.CSM.X.earth.err
## 692 TRUE
## 183 TRUE
## 368 FALSE
## 172 TRUE
## 488 TRUE
## 1613 TRUE
## sold.fctr.predict.CSM.X.earth.err.abs
## 692 0.90117647
## 183 0.90117647
## 368 0.09882353
## 172 0.46511628
## 488 0.65686275
## 1613 0.65686275
## sold.fctr.predict.CSM.X.earth.accurate
## 692 FALSE
## 183 FALSE
## 368 TRUE
## 172 FALSE
## 488 FALSE
## 1613 FALSE
## sold.fctr.predict.CSM.X.spatialSign.bagEarth.prob
## 692 0.11545744
## 183 0.09602702
## 368 0.07916056
## 172 0.51438577
## 488 0.69055820
## 1613 0.83207581
## sold.fctr.predict.CSM.X.spatialSign.bagEarth
## 692 N
## 183 N
## 368 N
## 172 Y
## 488 Y
## 1613 Y
## sold.fctr.predict.CSM.X.spatialSign.bagEarth.err
## 692 TRUE
## 183 TRUE
## 368 FALSE
## 172 TRUE
## 488 TRUE
## 1613 TRUE
## sold.fctr.predict.CSM.X.spatialSign.bagEarth.err.abs
## 692 0.88454256
## 183 0.90397298
## 368 0.07916056
## 172 0.51438577
## 488 0.69055820
## 1613 0.83207581
## sold.fctr.predict.CSM.X.spatialSign.bagEarth.accurate
## 692 FALSE
## 183 FALSE
## 368 TRUE
## 172 FALSE
## 488 FALSE
## 1613 FALSE
## sold.fctr.predict.CSM.X.nnet.prob sold.fctr.predict.CSM.X.nnet
## 692 0.23310739 N
## 183 0.39268544 N
## 368 0.04801783 N
## 172 0.84436443 Y
## 488 0.32191823 N
## 1613 0.76803024 Y
## sold.fctr.predict.CSM.X.nnet.err sold.fctr.predict.CSM.X.nnet.err.abs
## 692 TRUE 0.76689261
## 183 TRUE 0.60731456
## 368 FALSE 0.04801783
## 172 TRUE 0.84436443
## 488 FALSE 0.32191823
## 1613 TRUE 0.76803024
## sold.fctr.predict.CSM.X.nnet.accurate
## 692 FALSE
## 183 FALSE
## 368 TRUE
## 172 FALSE
## 488 TRUE
## 1613 FALSE
## sold.fctr.predict.CSM.X.avNNet.prob sold.fctr.predict.CSM.X.avNNet
## 692 0.1199940 N
## 183 0.1989900 N
## 368 0.1190849 N
## 172 0.5943533 Y
## 488 0.9686671 Y
## 1613 0.9429859 Y
## sold.fctr.predict.CSM.X.avNNet.err
## 692 TRUE
## 183 TRUE
## 368 FALSE
## 172 TRUE
## 488 TRUE
## 1613 TRUE
## sold.fctr.predict.CSM.X.avNNet.err.abs
## 692 0.8800060
## 183 0.8010100
## 368 0.1190849
## 172 0.5943533
## 488 0.9686671
## 1613 0.9429859
## sold.fctr.predict.CSM.X.avNNet.accurate
## 692 FALSE
## 183 FALSE
## 368 TRUE
## 172 FALSE
## 488 FALSE
## 1613 FALSE
## sold.fctr.predict.RFE.X.glm.prob sold.fctr.predict.RFE.X.glm
## 692 2.595014e-01 N
## 183 5.021409e-01 Y
## 368 3.273224e-02 N
## 172 8.142129e-01 Y
## 488 1.142393e-06 N
## 1613 7.417665e-01 Y
## sold.fctr.predict.RFE.X.glm.err sold.fctr.predict.RFE.X.glm.err.abs
## 692 TRUE 7.404986e-01
## 183 FALSE 4.978591e-01
## 368 FALSE 3.273224e-02
## 172 TRUE 8.142129e-01
## 488 FALSE 1.142393e-06
## 1613 TRUE 7.417665e-01
## sold.fctr.predict.RFE.X.glm.accurate sold.fctr.predict.CSM.X.glm.prob
## 692 FALSE 3.117194e-01
## 183 TRUE 4.949214e-01
## 368 TRUE 4.220261e-02
## 172 FALSE 8.023995e-01
## 488 TRUE 1.236938e-07
## 1613 FALSE 6.990131e-01
## sold.fctr.predict.CSM.X.glm sold.fctr.predict.CSM.X.glm.err
## 692 N TRUE
## 183 N TRUE
## 368 N FALSE
## 172 Y TRUE
## 488 N FALSE
## 1613 Y TRUE
## sold.fctr.predict.CSM.X.glm.err.abs
## 692 6.882806e-01
## 183 5.050786e-01
## 368 4.220261e-02
## 172 8.023995e-01
## 488 1.236938e-07
## 1613 6.990131e-01
## sold.fctr.predict.CSM.X.glm.accurate
## 692 FALSE
## 183 FALSE
## 368 TRUE
## 172 FALSE
## 488 TRUE
## 1613 FALSE
## sold.fctr.predict.CSM.X.glmnet.prob sold.fctr.predict.CSM.X.glmnet
## 692 0.17387655 N
## 183 0.24092586 N
## 368 0.05531553 N
## 172 0.71958515 Y
## 488 0.29392916 N
## 1613 0.77887863 Y
## sold.fctr.predict.CSM.X.glmnet.err
## 692 TRUE
## 183 TRUE
## 368 FALSE
## 172 TRUE
## 488 FALSE
## 1613 TRUE
## sold.fctr.predict.CSM.X.glmnet.err.abs
## 692 0.82612345
## 183 0.75907414
## 368 0.05531553
## 172 0.71958515
## 488 0.29392916
## 1613 0.77887863
## sold.fctr.predict.CSM.X.glmnet.accurate
## 692 FALSE
## 183 FALSE
## 368 TRUE
## 172 FALSE
## 488 TRUE
## 1613 FALSE
## sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.prob
## 692 0.1798914
## 183 0.1428484
## 368 0.1324950
## 172 0.1735153
## 488 0.7894978
## 1613 0.8846831
## sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet
## 692 N
## 183 N
## 368 N
## 172 N
## 488 Y
## 1613 Y
## sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.err
## 692 TRUE
## 183 TRUE
## 368 FALSE
## 172 FALSE
## 488 TRUE
## 1613 TRUE
## sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.err.abs
## 692 0.8201086
## 183 0.8571516
## 368 0.1324950
## 172 0.1735153
## 488 0.7894978
## 1613 0.8846831
## sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.accurate
## 692 FALSE
## 183 FALSE
## 368 TRUE
## 172 TRUE
## 488 FALSE
## 1613 FALSE
## sold.fctr.predict.CSM.X.gbm.prob sold.fctr.predict.CSM.X.gbm
## 692 0.02386776 N
## 183 0.13066265 N
## 368 0.28023962 N
## 172 0.66396950 Y
## 488 0.77010101 Y
## 1613 0.88086953 Y
## sold.fctr.predict.CSM.X.gbm.err sold.fctr.predict.CSM.X.gbm.err.abs
## 692 TRUE 0.9761322
## 183 TRUE 0.8693373
## 368 FALSE 0.2802396
## 172 TRUE 0.6639695
## 488 TRUE 0.7701010
## 1613 TRUE 0.8808695
## sold.fctr.predict.CSM.X.gbm.accurate
## 692 FALSE
## 183 FALSE
## 368 TRUE
## 172 FALSE
## 488 FALSE
## 1613 FALSE
## sold.fctr.predict.Interact.High.cor.Y.glmnet.prob
## 692 0.1933584
## 183 0.1704548
## 368 0.1636478
## 172 0.1895534
## 488 0.8094553
## 1613 0.9567789
## sold.fctr.predict.Interact.High.cor.Y.glmnet
## 692 N
## 183 N
## 368 N
## 172 N
## 488 Y
## 1613 Y
## sold.fctr.predict.Interact.High.cor.Y.glmnet.err
## 692 TRUE
## 183 TRUE
## 368 FALSE
## 172 FALSE
## 488 TRUE
## 1613 TRUE
## sold.fctr.predict.Interact.High.cor.Y.glmnet.err.abs
## 692 0.8066416
## 183 0.8295452
## 368 0.1636478
## 172 0.1895534
## 488 0.8094553
## 1613 0.9567789
## sold.fctr.predict.Interact.High.cor.Y.glmnet.accurate
## 692 FALSE
## 183 FALSE
## 368 TRUE
## 172 TRUE
## 488 FALSE
## 1613 FALSE
## sold.fctr.predict.Max.cor.Y.rpart.prob
## 692 0.1985560
## 183 0.1985560
## 368 0.1985560
## 172 0.1985560
## 488 0.9470199
## 1613 0.9470199
## sold.fctr.predict.Max.cor.Y.rpart
## 692 N
## 183 N
## 368 N
## 172 N
## 488 Y
## 1613 Y
## sold.fctr.predict.Max.cor.Y.rpart.err
## 692 TRUE
## 183 TRUE
## 368 FALSE
## 172 FALSE
## 488 TRUE
## 1613 TRUE
## sold.fctr.predict.Max.cor.Y.rpart.err.abs
## 692 0.8014440
## 183 0.8014440
## 368 0.1985560
## 172 0.1985560
## 488 0.9470199
## 1613 0.9470199
## sold.fctr.predict.Max.cor.Y.rpart.accurate
## 692 FALSE
## 183 FALSE
## 368 TRUE
## 172 TRUE
## 488 FALSE
## 1613 FALSE
## sold.fctr.predict.Low.cor.X.glmnet.prob
## 692 0.07213544
## 183 0.26515776
## 368 0.02957593
## 172 0.75520573
## 488 0.19736085
## 1613 0.75195170
## sold.fctr.predict.Low.cor.X.glmnet
## 692 N
## 183 N
## 368 N
## 172 Y
## 488 N
## 1613 Y
## sold.fctr.predict.Low.cor.X.glmnet.err
## 692 TRUE
## 183 TRUE
## 368 FALSE
## 172 TRUE
## 488 FALSE
## 1613 TRUE
## sold.fctr.predict.Low.cor.X.glmnet.err.abs
## 692 0.92786456
## 183 0.73484224
## 368 0.02957593
## 172 0.75520573
## 488 0.19736085
## 1613 0.75195170
## sold.fctr.predict.Low.cor.X.glmnet.accurate
## 692 FALSE
## 183 FALSE
## 368 TRUE
## 172 FALSE
## 488 TRUE
## 1613 FALSE
## sold.fctr.predict.CSM.X.bayesglm.prob
## 692 0.18368340
## 183 0.32166652
## 368 0.04960406
## 172 0.77305970
## 488 0.34447385
## 1613 0.75996782
## sold.fctr.predict.CSM.X.bayesglm sold.fctr.predict.CSM.X.bayesglm.err
## 692 N TRUE
## 183 Y FALSE
## 368 N FALSE
## 172 Y TRUE
## 488 Y TRUE
## 1613 Y TRUE
## sold.fctr.predict.CSM.X.bayesglm.err.abs
## 692 0.81631660
## 183 0.67833348
## 368 0.04960406
## 172 0.77305970
## 488 0.34447385
## 1613 0.75996782
## sold.fctr.predict.CSM.X.bayesglm.accurate
## 692 FALSE
## 183 TRUE
## 368 TRUE
## 172 FALSE
## 488 FALSE
## 1613 FALSE
## sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.prob
## 692 0.2757067
## 183 0.2479803
## 368 0.2395772
## 172 0.2711569
## 488 0.6746884
## 1613 0.7508944
## sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet
## 692 N
## 183 N
## 368 N
## 172 N
## 488 Y
## 1613 Y
## sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.err
## 692 TRUE
## 183 TRUE
## 368 FALSE
## 172 FALSE
## 488 TRUE
## 1613 TRUE
## sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.err.abs
## 692 0.7242933
## 183 0.7520197
## 368 0.2395772
## 172 0.2711569
## 488 0.6746884
## 1613 0.7508944
## sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.accurate
## 692 FALSE
## 183 FALSE
## 368 TRUE
## 172 TRUE
## 488 FALSE
## 1613 FALSE
## sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.prob
## 692 0.2669353
## 183 0.2379227
## 368 0.2291769
## 172 0.2621592
## 488 0.6847917
## 1613 0.7643508
## sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet
## 692 N
## 183 N
## 368 N
## 172 N
## 488 Y
## 1613 Y
## sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.err
## 692 TRUE
## 183 TRUE
## 368 FALSE
## 172 FALSE
## 488 TRUE
## 1613 TRUE
## sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.err.abs
## 692 0.7330647
## 183 0.7620773
## 368 0.2291769
## 172 0.2621592
## 488 0.6847917
## 1613 0.7643508
## sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.accurate
## 692 FALSE
## 183 FALSE
## 368 TRUE
## 172 TRUE
## 488 FALSE
## 1613 FALSE
## sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.prob
## 692 0.2857679
## 183 0.2597028
## 368 0.2517563
## 172 0.2815057
## 488 0.6632248
## 1613 0.7350085
## sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet
## 692 N
## 183 N
## 368 N
## 172 N
## 488 Y
## 1613 Y
## sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.err
## 692 TRUE
## 183 TRUE
## 368 FALSE
## 172 FALSE
## 488 TRUE
## 1613 TRUE
## sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.err.abs
## 692 0.7142321
## 183 0.7402972
## 368 0.2517563
## 172 0.2815057
## 488 0.6632248
## 1613 0.7350085
## sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.accurate
## 692 FALSE
## 183 FALSE
## 368 TRUE
## 172 TRUE
## 488 FALSE
## 1613 FALSE
## sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.prob
## 692 0.2974158
## 183 0.2735409
## 368 0.2662144
## 172 0.2935270
## 488 0.6501189
## 1613 0.7159720
## sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet
## 692 N
## 183 N
## 368 N
## 172 N
## 488 Y
## 1613 Y
## sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.err
## 692 TRUE
## 183 TRUE
## 368 FALSE
## 172 FALSE
## 488 TRUE
## 1613 TRUE
## sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.err.abs
## 692 0.7025842
## 183 0.7264591
## 368 0.2662144
## 172 0.2935270
## 488 0.6501189
## 1613 0.7159720
## sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.accurate
## 692 FALSE
## 183 FALSE
## 368 TRUE
## 172 TRUE
## 488 FALSE
## 1613 FALSE
## sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.prob
## 692 0.2974158
## 183 0.2735409
## 368 0.2662144
## 172 0.2935270
## 488 0.6501189
## 1613 0.7159720
## sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet
## 692 N
## 183 N
## 368 N
## 172 N
## 488 Y
## 1613 Y
## sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.err
## 692 TRUE
## 183 TRUE
## 368 FALSE
## 172 FALSE
## 488 TRUE
## 1613 TRUE
## sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.err.abs
## 692 0.7025842
## 183 0.7264591
## 368 0.2662144
## 172 0.2935270
## 488 0.6501189
## 1613 0.7159720
## sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.accurate
## 692 FALSE
## 183 FALSE
## 368 TRUE
## 172 TRUE
## 488 FALSE
## 1613 FALSE
## sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.prob
## 692 0.3106465
## 183 0.2897272
## 368 0.2832615
## 172 0.3072536
## 488 0.6354893
## 1613 0.6932376
## sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet
## 692 N
## 183 N
## 368 N
## 172 N
## 488 Y
## 1613 Y
## sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.err
## 692 TRUE
## 183 TRUE
## 368 FALSE
## 172 FALSE
## 488 TRUE
## 1613 TRUE
## sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.err.abs
## 692 0.6893535
## 183 0.7102728
## 368 0.2832615
## 172 0.3072536
## 488 0.6354893
## 1613 0.6932376
## sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.accurate
## 692 FALSE
## 183 FALSE
## 368 TRUE
## 172 TRUE
## 488 FALSE
## 1613 FALSE
## sold.fctr.predict.RFE.X.glmnet.prob sold.fctr.predict.RFE.X.glmnet
## 692 0.15295923 N
## 183 0.31899426 Y
## 368 0.03561934 N
## 172 0.78476950 Y
## 488 0.17790031 N
## 1613 0.78709125 Y
## sold.fctr.predict.RFE.X.glmnet.err
## 692 TRUE
## 183 FALSE
## 368 FALSE
## 172 TRUE
## 488 FALSE
## 1613 TRUE
## sold.fctr.predict.RFE.X.glmnet.err.abs
## 692 0.84704077
## 183 0.68100574
## 368 0.03561934
## 172 0.78476950
## 488 0.17790031
## 1613 0.78709125
## sold.fctr.predict.RFE.X.glmnet.accurate
## 692 FALSE
## 183 TRUE
## 368 TRUE
## 172 FALSE
## 488 TRUE
## 1613 FALSE
## sold.fctr.predict.All.X.glmnet.prob sold.fctr.predict.All.X.glmnet
## 692 0.18131809 N
## 183 0.37830215 Y
## 368 0.03103741 N
## 172 0.81040623 Y
## 488 0.05331650 N
## 1613 0.77057825 Y
## sold.fctr.predict.All.X.glmnet.err
## 692 TRUE
## 183 FALSE
## 368 FALSE
## 172 TRUE
## 488 FALSE
## 1613 TRUE
## sold.fctr.predict.All.X.glmnet.err.abs
## 692 0.81868191
## 183 0.62169785
## 368 0.03103741
## 172 0.81040623
## 488 0.05331650
## 1613 0.77057825
## sold.fctr.predict.All.X.glmnet.accurate
## 692 FALSE
## 183 TRUE
## 368 TRUE
## 172 FALSE
## 488 TRUE
## 1613 FALSE
## sold.fctr.predict.CSM.X.rpart.prob sold.fctr.predict.CSM.X.rpart
## 692 0.1971067 N
## 183 0.1971067 N
## 368 0.1971067 N
## 172 0.1971067 N
## 488 0.9470199 Y
## 1613 0.9470199 Y
## sold.fctr.predict.CSM.X.rpart.err
## 692 TRUE
## 183 TRUE
## 368 FALSE
## 172 FALSE
## 488 TRUE
## 1613 TRUE
## sold.fctr.predict.CSM.X.rpart.err.abs
## 692 0.8028933
## 183 0.8028933
## 368 0.1971067
## 172 0.1971067
## 488 0.9470199
## 1613 0.9470199
## sold.fctr.predict.CSM.X.rpart.accurate
## 692 FALSE
## 183 FALSE
## 368 TRUE
## 172 TRUE
## 488 FALSE
## 1613 FALSE
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.prob
## 692 0.1939163
## 183 0.1939163
## 368 0.1939163
## 172 0.1939163
## 488 0.9470199
## 1613 0.9470199
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart
## 692 N
## 183 N
## 368 N
## 172 N
## 488 Y
## 1613 Y
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.err
## 692 TRUE
## 183 TRUE
## 368 FALSE
## 172 FALSE
## 488 TRUE
## 1613 TRUE
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.err.abs
## 692 0.8060837
## 183 0.8060837
## 368 0.1939163
## 172 0.1939163
## 488 0.9470199
## 1613 0.9470199
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.accurate
## 692 FALSE
## 183 FALSE
## 368 TRUE
## 172 TRUE
## 488 FALSE
## 1613 FALSE
## sold.fctr.predict.Ensemble.repeatedcv.glmnet.prob
## 692 0.02292188
## 183 0.09128792
## 368 0.28506123
## 172 0.92434382
## 488 0.97397286
## 1613 0.98149810
## sold.fctr.predict.Ensemble.repeatedcv.glmnet
## 692 N
## 183 N
## 368 Y
## 172 Y
## 488 Y
## 1613 Y
## sold.fctr.predict.Ensemble.repeatedcv.glmnet.err
## 692 TRUE
## 183 TRUE
## 368 TRUE
## 172 TRUE
## 488 TRUE
## 1613 TRUE
## sold.fctr.predict.Ensemble.repeatedcv.glmnet.err.abs
## 692 0.9770781
## 183 0.9087121
## 368 0.2850612
## 172 0.9243438
## 488 0.9739729
## 1613 0.9814981
## sold.fctr.predict.Ensemble.repeatedcv.glmnet.accurate
## 692 FALSE
## 183 FALSE
## 368 FALSE
## 172 FALSE
## 488 FALSE
## 1613 FALSE
## sold.fctr.predict.Ensemble.repeatedcv.glmnet.error
## 692 -0.17707812
## 183 -0.10871208
## 368 0.08506123
## 172 0.72434382
## 488 0.77397286
## 1613 0.78149810
## UniqueID sold.fctr sold.fctr.predict.CSM.X.center.scale.bagEarth.prob
## 594 10595 N 0.9573058
## 525 10526 N 0.8222107
## 412 10413 N 0.9835617
## 1691 11700 N 0.9455479
## 1373 11380 N 0.5539662
## 1085 11090 N 0.7073323
## sold.fctr.predict.CSM.X.center.scale.bagEarth
## 594 Y
## 525 Y
## 412 Y
## 1691 Y
## 1373 Y
## 1085 Y
## sold.fctr.predict.CSM.X.center.scale.bagEarth.err
## 594 TRUE
## 525 TRUE
## 412 TRUE
## 1691 TRUE
## 1373 TRUE
## 1085 TRUE
## sold.fctr.predict.CSM.X.center.scale.bagEarth.err.abs
## 594 0.9573058
## 525 0.8222107
## 412 0.9835617
## 1691 0.9455479
## 1373 0.5539662
## 1085 0.7073323
## sold.fctr.predict.CSM.X.center.scale.bagEarth.accurate
## 594 FALSE
## 525 FALSE
## 412 FALSE
## 1691 FALSE
## 1373 FALSE
## 1085 FALSE
## sold.fctr.predict.CSM.X.bagEarth.prob
## 594 0.9581184
## 525 0.7693755
## 412 0.9837734
## 1691 0.9382670
## 1373 0.5651721
## 1085 0.6540987
## sold.fctr.predict.CSM.X.bagEarth sold.fctr.predict.CSM.X.bagEarth.err
## 594 Y TRUE
## 525 Y TRUE
## 412 Y TRUE
## 1691 Y TRUE
## 1373 Y TRUE
## 1085 Y TRUE
## sold.fctr.predict.CSM.X.bagEarth.err.abs
## 594 0.9581184
## 525 0.7693755
## 412 0.9837734
## 1691 0.9382670
## 1373 0.5651721
## 1085 0.6540987
## sold.fctr.predict.CSM.X.bagEarth.accurate
## 594 FALSE
## 525 FALSE
## 412 FALSE
## 1691 FALSE
## 1373 FALSE
## 1085 FALSE
## sold.fctr.predict.CSM.X.Interact.glmnet.prob
## 594 0.8469901
## 525 0.6029806
## 412 0.8791269
## 1691 0.8472311
## 1373 0.4727173
## 1085 0.1625680
## sold.fctr.predict.CSM.X.Interact.glmnet
## 594 Y
## 525 Y
## 412 Y
## 1691 Y
## 1373 Y
## 1085 N
## sold.fctr.predict.CSM.X.Interact.glmnet.err
## 594 TRUE
## 525 TRUE
## 412 TRUE
## 1691 TRUE
## 1373 TRUE
## 1085 FALSE
## sold.fctr.predict.CSM.X.Interact.glmnet.err.abs
## 594 0.8469901
## 525 0.6029806
## 412 0.8791269
## 1691 0.8472311
## 1373 0.4727173
## 1085 0.1625680
## sold.fctr.predict.CSM.X.Interact.glmnet.accurate
## 594 FALSE
## 525 FALSE
## 412 FALSE
## 1691 FALSE
## 1373 FALSE
## 1085 TRUE
## sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.prob
## 594 0.9701298
## 525 0.8215797
## 412 0.9905558
## 1691 0.9455734
## 1373 0.5966730
## 1085 0.5640889
## sold.fctr.predict.CSM.X.YeoJohnson.bagEarth
## 594 Y
## 525 Y
## 412 Y
## 1691 Y
## 1373 Y
## 1085 Y
## sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.err
## 594 TRUE
## 525 TRUE
## 412 TRUE
## 1691 TRUE
## 1373 TRUE
## 1085 TRUE
## sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.err.abs
## 594 0.9701298
## 525 0.8215797
## 412 0.9905558
## 1691 0.9455734
## 1373 0.5966730
## 1085 0.5640889
## sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.accurate
## 594 FALSE
## 525 FALSE
## 412 FALSE
## 1691 FALSE
## 1373 FALSE
## 1085 FALSE
## sold.fctr.predict.CSM.X.earth.prob sold.fctr.predict.CSM.X.earth
## 594 0.96666667 Y
## 525 0.65686275 Y
## 412 0.97530864 Y
## 1691 0.97530864 Y
## 1373 0.46341463 Y
## 1085 0.09882353 N
## sold.fctr.predict.CSM.X.earth.err
## 594 TRUE
## 525 TRUE
## 412 TRUE
## 1691 TRUE
## 1373 TRUE
## 1085 FALSE
## sold.fctr.predict.CSM.X.earth.err.abs
## 594 0.96666667
## 525 0.65686275
## 412 0.97530864
## 1691 0.97530864
## 1373 0.46341463
## 1085 0.09882353
## sold.fctr.predict.CSM.X.earth.accurate
## 594 FALSE
## 525 FALSE
## 412 FALSE
## 1691 FALSE
## 1373 FALSE
## 1085 TRUE
## sold.fctr.predict.CSM.X.spatialSign.bagEarth.prob
## 594 0.9327735
## 525 0.7628488
## 412 0.9454580
## 1691 0.9834084
## 1373 0.3746286
## 1085 0.1822745
## sold.fctr.predict.CSM.X.spatialSign.bagEarth
## 594 Y
## 525 Y
## 412 Y
## 1691 Y
## 1373 N
## 1085 N
## sold.fctr.predict.CSM.X.spatialSign.bagEarth.err
## 594 TRUE
## 525 TRUE
## 412 TRUE
## 1691 TRUE
## 1373 FALSE
## 1085 FALSE
## sold.fctr.predict.CSM.X.spatialSign.bagEarth.err.abs
## 594 0.9327735
## 525 0.7628488
## 412 0.9454580
## 1691 0.9834084
## 1373 0.3746286
## 1085 0.1822745
## sold.fctr.predict.CSM.X.spatialSign.bagEarth.accurate
## 594 FALSE
## 525 FALSE
## 412 FALSE
## 1691 FALSE
## 1373 TRUE
## 1085 TRUE
## sold.fctr.predict.CSM.X.nnet.prob sold.fctr.predict.CSM.X.nnet
## 594 0.9605986 Y
## 525 0.7290553 Y
## 412 0.9765208 Y
## 1691 0.9534211 Y
## 1373 0.5331962 Y
## 1085 0.7775603 Y
## sold.fctr.predict.CSM.X.nnet.err sold.fctr.predict.CSM.X.nnet.err.abs
## 594 TRUE 0.9605986
## 525 TRUE 0.7290553
## 412 TRUE 0.9765208
## 1691 TRUE 0.9534211
## 1373 TRUE 0.5331962
## 1085 TRUE 0.7775603
## sold.fctr.predict.CSM.X.nnet.accurate
## 594 FALSE
## 525 FALSE
## 412 FALSE
## 1691 FALSE
## 1373 FALSE
## 1085 FALSE
## sold.fctr.predict.CSM.X.avNNet.prob sold.fctr.predict.CSM.X.avNNet
## 594 0.9945824 Y
## 525 0.8396235 Y
## 412 0.9927009 Y
## 1691 0.9964940 Y
## 1373 0.3637136 Y
## 1085 0.4115566 Y
## sold.fctr.predict.CSM.X.avNNet.err
## 594 TRUE
## 525 TRUE
## 412 TRUE
## 1691 TRUE
## 1373 TRUE
## 1085 TRUE
## sold.fctr.predict.CSM.X.avNNet.err.abs
## 594 0.9945824
## 525 0.8396235
## 412 0.9927009
## 1691 0.9964940
## 1373 0.3637136
## 1085 0.4115566
## sold.fctr.predict.CSM.X.avNNet.accurate
## 594 FALSE
## 525 FALSE
## 412 FALSE
## 1691 FALSE
## 1373 FALSE
## 1085 FALSE
## sold.fctr.predict.RFE.X.glm.prob sold.fctr.predict.RFE.X.glm
## 594 0.9362250 Y
## 525 0.8178503 Y
## 412 0.9765885 Y
## 1691 0.5592489 Y
## 1373 0.4673416 N
## 1085 0.5093008 Y
## sold.fctr.predict.RFE.X.glm.err sold.fctr.predict.RFE.X.glm.err.abs
## 594 TRUE 0.9362250
## 525 TRUE 0.8178503
## 412 TRUE 0.9765885
## 1691 TRUE 0.5592489
## 1373 FALSE 0.4673416
## 1085 TRUE 0.5093008
## sold.fctr.predict.RFE.X.glm.accurate sold.fctr.predict.CSM.X.glm.prob
## 594 FALSE 0.9410950
## 525 FALSE 0.7922377
## 412 FALSE 0.9914893
## 1691 FALSE 0.8042050
## 1373 TRUE 0.5503513
## 1085 FALSE 0.5242297
## sold.fctr.predict.CSM.X.glm sold.fctr.predict.CSM.X.glm.err
## 594 Y TRUE
## 525 Y TRUE
## 412 Y TRUE
## 1691 Y TRUE
## 1373 Y TRUE
## 1085 Y TRUE
## sold.fctr.predict.CSM.X.glm.err.abs
## 594 0.9410950
## 525 0.7922377
## 412 0.9914893
## 1691 0.8042050
## 1373 0.5503513
## 1085 0.5242297
## sold.fctr.predict.CSM.X.glm.accurate
## 594 FALSE
## 525 FALSE
## 412 FALSE
## 1691 FALSE
## 1373 FALSE
## 1085 FALSE
## sold.fctr.predict.CSM.X.glmnet.prob sold.fctr.predict.CSM.X.glmnet
## 594 0.9219797 Y
## 525 0.5887013 Y
## 412 0.9819210 Y
## 1691 0.8937591 Y
## 1373 0.5678496 Y
## 1085 0.3619130 N
## sold.fctr.predict.CSM.X.glmnet.err
## 594 TRUE
## 525 TRUE
## 412 TRUE
## 1691 TRUE
## 1373 TRUE
## 1085 FALSE
## sold.fctr.predict.CSM.X.glmnet.err.abs
## 594 0.9219797
## 525 0.5887013
## 412 0.9819210
## 1691 0.8937591
## 1373 0.5678496
## 1085 0.3619130
## sold.fctr.predict.CSM.X.glmnet.accurate
## 594 FALSE
## 525 FALSE
## 412 FALSE
## 1691 FALSE
## 1373 FALSE
## 1085 TRUE
## sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.prob
## 594 0.7724491
## 525 0.8305945
## 412 0.7633035
## 1691 0.8999581
## 1373 0.2518773
## 1085 0.1661108
## sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet
## 594 Y
## 525 Y
## 412 Y
## 1691 Y
## 1373 N
## 1085 N
## sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.err
## 594 TRUE
## 525 TRUE
## 412 TRUE
## 1691 TRUE
## 1373 FALSE
## 1085 FALSE
## sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.err.abs
## 594 0.7724491
## 525 0.8305945
## 412 0.7633035
## 1691 0.8999581
## 1373 0.2518773
## 1085 0.1661108
## sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.accurate
## 594 FALSE
## 525 FALSE
## 412 FALSE
## 1691 FALSE
## 1373 TRUE
## 1085 TRUE
## sold.fctr.predict.CSM.X.gbm.prob sold.fctr.predict.CSM.X.gbm
## 594 0.9284190 Y
## 525 0.9482263 Y
## 412 0.9675679 Y
## 1691 0.9574127 Y
## 1373 0.8220978 Y
## 1085 0.8750109 Y
## sold.fctr.predict.CSM.X.gbm.err sold.fctr.predict.CSM.X.gbm.err.abs
## 594 TRUE 0.9284190
## 525 TRUE 0.9482263
## 412 TRUE 0.9675679
## 1691 TRUE 0.9574127
## 1373 TRUE 0.8220978
## 1085 TRUE 0.8750109
## sold.fctr.predict.CSM.X.gbm.accurate
## 594 FALSE
## 525 FALSE
## 412 FALSE
## 1691 FALSE
## 1373 FALSE
## 1085 FALSE
## sold.fctr.predict.Interact.High.cor.Y.glmnet.prob
## 594 0.8466358
## 525 0.8824372
## 412 0.8352926
## 1691 0.9110882
## 1373 0.2336056
## 1085 0.1850694
## sold.fctr.predict.Interact.High.cor.Y.glmnet
## 594 Y
## 525 Y
## 412 Y
## 1691 Y
## 1373 N
## 1085 N
## sold.fctr.predict.Interact.High.cor.Y.glmnet.err
## 594 TRUE
## 525 TRUE
## 412 TRUE
## 1691 TRUE
## 1373 FALSE
## 1085 FALSE
## sold.fctr.predict.Interact.High.cor.Y.glmnet.err.abs
## 594 0.8466358
## 525 0.8824372
## 412 0.8352926
## 1691 0.9110882
## 1373 0.2336056
## 1085 0.1850694
## sold.fctr.predict.Interact.High.cor.Y.glmnet.accurate
## 594 FALSE
## 525 FALSE
## 412 FALSE
## 1691 FALSE
## 1373 TRUE
## 1085 TRUE
## sold.fctr.predict.Max.cor.Y.rpart.prob
## 594 0.9470199
## 525 0.9470199
## 412 0.9470199
## 1691 0.9470199
## 1373 0.1985560
## 1085 0.1985560
## sold.fctr.predict.Max.cor.Y.rpart
## 594 Y
## 525 Y
## 412 Y
## 1691 Y
## 1373 N
## 1085 N
## sold.fctr.predict.Max.cor.Y.rpart.err
## 594 TRUE
## 525 TRUE
## 412 TRUE
## 1691 TRUE
## 1373 FALSE
## 1085 FALSE
## sold.fctr.predict.Max.cor.Y.rpart.err.abs
## 594 0.9470199
## 525 0.9470199
## 412 0.9470199
## 1691 0.9470199
## 1373 0.1985560
## 1085 0.1985560
## sold.fctr.predict.Max.cor.Y.rpart.accurate
## 594 FALSE
## 525 FALSE
## 412 FALSE
## 1691 FALSE
## 1373 TRUE
## 1085 TRUE
## sold.fctr.predict.Low.cor.X.glmnet.prob
## 594 0.9271885
## 525 0.5791774
## 412 0.9700054
## 1691 0.7704952
## 1373 0.4773375
## 1085 0.3214960
## sold.fctr.predict.Low.cor.X.glmnet
## 594 Y
## 525 Y
## 412 Y
## 1691 Y
## 1373 Y
## 1085 Y
## sold.fctr.predict.Low.cor.X.glmnet.err
## 594 TRUE
## 525 TRUE
## 412 TRUE
## 1691 TRUE
## 1373 TRUE
## 1085 TRUE
## sold.fctr.predict.Low.cor.X.glmnet.err.abs
## 594 0.9271885
## 525 0.5791774
## 412 0.9700054
## 1691 0.7704952
## 1373 0.4773375
## 1085 0.3214960
## sold.fctr.predict.Low.cor.X.glmnet.accurate
## 594 FALSE
## 525 FALSE
## 412 FALSE
## 1691 FALSE
## 1373 FALSE
## 1085 FALSE
## sold.fctr.predict.CSM.X.bayesglm.prob
## 594 0.9298323
## 525 0.6595602
## 412 0.9883015
## 1691 0.8778029
## 1373 0.5796021
## 1085 0.4311118
## sold.fctr.predict.CSM.X.bayesglm sold.fctr.predict.CSM.X.bayesglm.err
## 594 Y TRUE
## 525 Y TRUE
## 412 Y TRUE
## 1691 Y TRUE
## 1373 Y TRUE
## 1085 Y TRUE
## sold.fctr.predict.CSM.X.bayesglm.err.abs
## 594 0.9298323
## 525 0.6595602
## 412 0.9883015
## 1691 0.8778029
## 1373 0.5796021
## 1085 0.4311118
## sold.fctr.predict.CSM.X.bayesglm.accurate
## 594 FALSE
## 525 FALSE
## 412 FALSE
## 1691 FALSE
## 1373 FALSE
## 1085 FALSE
## sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.prob
## 594 0.6631515
## 525 0.7046349
## 412 0.6571362
## 1691 0.7661282
## 1373 0.3225688
## 1085 0.2657668
## sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet
## 594 Y
## 525 Y
## 412 Y
## 1691 Y
## 1373 N
## 1085 N
## sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.err
## 594 TRUE
## 525 TRUE
## 412 TRUE
## 1691 TRUE
## 1373 FALSE
## 1085 FALSE
## sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.err.abs
## 594 0.6631515
## 525 0.7046349
## 412 0.6571362
## 1691 0.7661282
## 1373 0.3225688
## 1085 0.2657668
## sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.accurate
## 594 FALSE
## 525 FALSE
## 412 FALSE
## 1691 FALSE
## 1373 TRUE
## 1085 TRUE
## sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.prob
## 594 0.6726155
## 525 0.7162520
## 412 0.6662558
## 1691 0.7800317
## 1373 0.3164214
## 1085 0.2565084
## sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet
## 594 Y
## 525 Y
## 412 Y
## 1691 Y
## 1373 N
## 1085 N
## sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.err
## 594 TRUE
## 525 TRUE
## 412 TRUE
## 1691 TRUE
## 1373 FALSE
## 1085 FALSE
## sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.err.abs
## 594 0.6726155
## 525 0.7162520
## 412 0.6662558
## 1691 0.7800317
## 1373 0.3164214
## 1085 0.2565084
## sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.accurate
## 594 FALSE
## 525 FALSE
## 412 FALSE
## 1691 FALSE
## 1373 TRUE
## 1085 TRUE
## sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.prob
## 594 0.6524850
## 525 0.6912413
## 412 0.6468959
## 1691 0.7495811
## 1373 0.3293822
## 1085 0.2764491
## sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet
## 594 Y
## 525 Y
## 412 Y
## 1691 Y
## 1373 N
## 1085 N
## sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.err
## 594 TRUE
## 525 TRUE
## 412 TRUE
## 1691 TRUE
## 1373 FALSE
## 1085 FALSE
## sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.err.abs
## 594 0.6524850
## 525 0.6912413
## 412 0.6468959
## 1691 0.7495811
## 1373 0.3293822
## 1085 0.2764491
## sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.accurate
## 594 FALSE
## 525 FALSE
## 412 FALSE
## 1691 FALSE
## 1373 TRUE
## 1085 TRUE
## sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.prob
## 594 0.6403921
## 525 0.6756298
## 412 0.6353402
## 1691 0.7295666
## 1373 0.3369253
## 1085 0.2889059
## sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet
## 594 Y
## 525 Y
## 412 Y
## 1691 Y
## 1373 N
## 1085 N
## sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.err
## 594 TRUE
## 525 TRUE
## 412 TRUE
## 1691 TRUE
## 1373 FALSE
## 1085 FALSE
## sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.err.abs
## 594 0.6403921
## 525 0.6756298
## 412 0.6353402
## 1691 0.7295666
## 1373 0.3369253
## 1085 0.2889059
## sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.accurate
## 594 FALSE
## 525 FALSE
## 412 FALSE
## 1691 FALSE
## 1373 TRUE
## 1085 TRUE
## sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.prob
## 594 0.6403921
## 525 0.6756298
## 412 0.6353402
## 1691 0.7295666
## 1373 0.3369253
## 1085 0.2889059
## sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet
## 594 Y
## 525 Y
## 412 Y
## 1691 Y
## 1373 N
## 1085 N
## sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.err
## 594 TRUE
## 525 TRUE
## 412 TRUE
## 1691 TRUE
## 1373 FALSE
## 1085 FALSE
## sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.err.abs
## 594 0.6403921
## 525 0.6756298
## 412 0.6353402
## 1691 0.7295666
## 1373 0.3369253
## 1085 0.2889059
## sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.accurate
## 594 FALSE
## 525 FALSE
## 412 FALSE
## 1691 FALSE
## 1373 TRUE
## 1085 TRUE
## sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.prob
## 594 0.6270752
## 525 0.6576826
## 412 0.6227143
## 1691 0.7053743
## 1373 0.3448489
## 1085 0.3032149
## sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet
## 594 Y
## 525 Y
## 412 Y
## 1691 Y
## 1373 N
## 1085 N
## sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.err
## 594 TRUE
## 525 TRUE
## 412 TRUE
## 1691 TRUE
## 1373 FALSE
## 1085 FALSE
## sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.err.abs
## 594 0.6270752
## 525 0.6576826
## 412 0.6227143
## 1691 0.7053743
## 1373 0.3448489
## 1085 0.3032149
## sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.accurate
## 594 FALSE
## 525 FALSE
## 412 FALSE
## 1691 FALSE
## 1373 TRUE
## 1085 TRUE
## sold.fctr.predict.RFE.X.glmnet.prob sold.fctr.predict.RFE.X.glmnet
## 594 0.9173801 Y
## 525 0.6162055 Y
## 412 0.9670848 Y
## 1691 0.7002921 Y
## 1373 0.5339213 Y
## 1085 0.3827787 Y
## sold.fctr.predict.RFE.X.glmnet.err
## 594 TRUE
## 525 TRUE
## 412 TRUE
## 1691 TRUE
## 1373 TRUE
## 1085 TRUE
## sold.fctr.predict.RFE.X.glmnet.err.abs
## 594 0.9173801
## 525 0.6162055
## 412 0.9670848
## 1691 0.7002921
## 1373 0.5339213
## 1085 0.3827787
## sold.fctr.predict.RFE.X.glmnet.accurate
## 594 FALSE
## 525 FALSE
## 412 FALSE
## 1691 FALSE
## 1373 FALSE
## 1085 FALSE
## sold.fctr.predict.All.X.glmnet.prob sold.fctr.predict.All.X.glmnet
## 594 0.9220160 Y
## 525 0.6909521 Y
## 412 0.9743568 Y
## 1691 0.6179721 Y
## 1373 0.5256284 Y
## 1085 0.4075837 Y
## sold.fctr.predict.All.X.glmnet.err
## 594 TRUE
## 525 TRUE
## 412 TRUE
## 1691 TRUE
## 1373 TRUE
## 1085 TRUE
## sold.fctr.predict.All.X.glmnet.err.abs
## 594 0.9220160
## 525 0.6909521
## 412 0.9743568
## 1691 0.6179721
## 1373 0.5256284
## 1085 0.4075837
## sold.fctr.predict.All.X.glmnet.accurate
## 594 FALSE
## 525 FALSE
## 412 FALSE
## 1691 FALSE
## 1373 FALSE
## 1085 FALSE
## sold.fctr.predict.CSM.X.rpart.prob sold.fctr.predict.CSM.X.rpart
## 594 0.9470199 Y
## 525 0.9470199 Y
## 412 0.9470199 Y
## 1691 0.9470199 Y
## 1373 0.1971067 N
## 1085 0.1971067 N
## sold.fctr.predict.CSM.X.rpart.err
## 594 TRUE
## 525 TRUE
## 412 TRUE
## 1691 TRUE
## 1373 FALSE
## 1085 FALSE
## sold.fctr.predict.CSM.X.rpart.err.abs
## 594 0.9470199
## 525 0.9470199
## 412 0.9470199
## 1691 0.9470199
## 1373 0.1971067
## 1085 0.1971067
## sold.fctr.predict.CSM.X.rpart.accurate
## 594 FALSE
## 525 FALSE
## 412 FALSE
## 1691 FALSE
## 1373 TRUE
## 1085 TRUE
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.prob
## 594 0.9470199
## 525 0.9470199
## 412 0.9470199
## 1691 0.9470199
## 1373 0.7777778
## 1085 0.1939163
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart
## 594 Y
## 525 Y
## 412 Y
## 1691 Y
## 1373 Y
## 1085 N
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.err
## 594 TRUE
## 525 TRUE
## 412 TRUE
## 1691 TRUE
## 1373 TRUE
## 1085 FALSE
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.err.abs
## 594 0.9470199
## 525 0.9470199
## 412 0.9470199
## 1691 0.9470199
## 1373 0.7777778
## 1085 0.1939163
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.accurate
## 594 FALSE
## 525 FALSE
## 412 FALSE
## 1691 FALSE
## 1373 FALSE
## 1085 TRUE
## sold.fctr.predict.Ensemble.repeatedcv.glmnet.prob
## 594 0.9909127
## 525 0.9919933
## 412 0.9922454
## 1691 0.9933222
## 1373 0.9961029
## 1085 0.9979883
## sold.fctr.predict.Ensemble.repeatedcv.glmnet
## 594 Y
## 525 Y
## 412 Y
## 1691 Y
## 1373 Y
## 1085 Y
## sold.fctr.predict.Ensemble.repeatedcv.glmnet.err
## 594 TRUE
## 525 TRUE
## 412 TRUE
## 1691 TRUE
## 1373 TRUE
## 1085 TRUE
## sold.fctr.predict.Ensemble.repeatedcv.glmnet.err.abs
## 594 0.9909127
## 525 0.9919933
## 412 0.9922454
## 1691 0.9933222
## 1373 0.9961029
## 1085 0.9979883
## sold.fctr.predict.Ensemble.repeatedcv.glmnet.accurate
## 594 FALSE
## 525 FALSE
## 412 FALSE
## 1691 FALSE
## 1373 FALSE
## 1085 FALSE
## sold.fctr.predict.Ensemble.repeatedcv.glmnet.error
## 594 0.7909127
## 525 0.7919933
## 412 0.7922454
## 1691 0.7933222
## 1373 0.7961029
## 1085 0.7979883
glb_ctgry_df <- merge(glb_ctgry_df,
myget_category_stats(obs_df = glb_fitobs_df, mdl_id = glb_sel_mdl_id, label = "fit"),
by = glb_category_var, all = TRUE)
row.names(glb_ctgry_df) <- glb_ctgry_df[, glb_category_var]
glb_ctgry_df <- merge(glb_ctgry_df,
myget_category_stats(obs_df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id, label="OOB"),
#by=glb_category_var, all=TRUE) glb_ctgry-df already contains .n.OOB ?
all=TRUE)
row.names(glb_ctgry_df) <- glb_ctgry_df[, glb_category_var]
if (any(grepl("OOB", glbMdlMetricsEval)))
print(orderBy(~-err.abs.OOB.mean, glb_ctgry_df)) else
print(orderBy(~-err.abs.fit.mean, glb_ctgry_df))## prdl.my.fctr .n.OOB .n.Fit .n.Tst .freqRatio.Fit .freqRatio.OOB
## iPadmini2 iPadmini2 53 54 56 0.05294118 0.06385542
## iPad1 iPad1 95 130 88 0.12745098 0.11445783
## iPad3 iPad3 61 92 55 0.09019608 0.07349398
## iPadmini iPadmini 123 154 111 0.15098039 0.14819277
## iPadmini3 iPadmini3 36 54 38 0.05294118 0.04337349
## iPad4 iPad4 73 84 68 0.08235294 0.08795181
## iPadAir2 iPadAir2 69 102 62 0.10000000 0.08313253
## Unknown Unknown 96 108 92 0.10588235 0.11566265
## iPad2 iPad2 142 144 154 0.14117647 0.17108434
## iPadAir iPadAir 82 98 74 0.09607843 0.09879518
## .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean .n.fit
## iPadmini2 0.07017544 6.561706 0.12151308 54
## iPad1 0.11027569 8.833084 0.06794680 130
## iPad3 0.06892231 10.931711 0.11882295 92
## iPadmini 0.13909774 16.408094 0.10654606 154
## iPadmini3 0.04761905 7.819824 0.14481155 54
## iPad4 0.08521303 6.474186 0.07707364 84
## iPadAir2 0.07769424 12.850296 0.12598329 102
## Unknown 0.11528822 18.317750 0.16960880 108
## iPad2 0.19298246 11.492253 0.07980731 144
## iPadAir 0.09273183 6.386205 0.06516536 98
## err.abs.OOB.sum err.abs.OOB.mean
## iPadmini2 19.13157 0.3609729
## iPad1 25.83490 0.2719463
## iPad3 16.04787 0.2630799
## iPadmini 31.95087 0.2597632
## iPadmini3 9.28349 0.2578747
## iPad4 18.11110 0.2480973
## iPadAir2 16.83049 0.2439201
## Unknown 22.85216 0.2380434
## iPad2 31.11043 0.2190875
## iPadAir 16.93299 0.2064999print(colSums(glb_ctgry_df[, -grep(glb_category_var, names(glb_ctgry_df))]))## .n.OOB .n.Fit .n.Tst .freqRatio.Fit
## 830.000000 1020.000000 798.000000 1.000000
## .freqRatio.OOB .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean
## 1.000000 1.000000 106.075110 1.077279
## .n.fit err.abs.OOB.sum err.abs.OOB.mean
## 1020.000000 208.085884 2.569285write.csv(glb_OOBobs_df[, c(glb_id_var,
grep(glb_rsp_var, names(glb_OOBobs_df), fixed=TRUE, value=TRUE))],
paste0(gsub(".", "_", paste0(glb_out_pfx, glb_sel_mdl_id), fixed=TRUE),
"_OOBobs.csv"), row.names=FALSE)
fit.models_2_chunk_df <-
myadd_chunk(NULL, "fit.models_2_bgn", label.minor = "teardown")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_2_bgn 1 0 teardown 1488.092 NA NAglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor label_minor bgn end elapsed
## 12 fit.models 6 2 2 1302.250 1488.118 185.868
## 13 fit.models 6 3 3 1488.119 NA NA# if (sum(is.na(glb_allobs_df$D.P.http)) > 0)
# stop("fit.models_3: Why is this happening ?")
#stop(here"); glb_to_sav()
sync_glb_obs_df <- function() {
# Merge or cbind ?
for (col in setdiff(names(glb_fitobs_df), names(glb_trnobs_df)))
glb_trnobs_df[glb_trnobs_df$.lcn == "Fit", col] <<- glb_fitobs_df[, col]
for (col in setdiff(names(glb_fitobs_df), names(glb_allobs_df)))
glb_allobs_df[glb_allobs_df$.lcn == "Fit", col] <<- glb_fitobs_df[, col]
if (all(is.na(glb_newobs_df[, glb_rsp_var])))
for (col in setdiff(names(glb_OOBobs_df), names(glb_trnobs_df)))
glb_trnobs_df[glb_trnobs_df$.lcn == "OOB", col] <<- glb_OOBobs_df[, col]
for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <<- glb_OOBobs_df[, col]
}
sync_glb_obs_df()
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df,
glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_sel_mdl, glb_sel_mdl_id,
glb_model_type,
file=paste0(glb_out_pfx, "selmdl_dsk.RData"))
#load(paste0(glb_out_pfx, "selmdl_dsk.RData"))
rm(ret_lst)
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"model.selected")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=TRUE)## label step_major step_minor label_minor bgn end
## 13 fit.models 6 3 3 1488.119 1519.887
## 14 fit.data.training 7 0 0 1519.888 NA
## elapsed
## 13 31.769
## 14 NAStep 7.0: fit data training
#load(paste0(glb_inp_pfx, "dsk.RData"))
#stop(here"); glb_to_sav()
if (!is.null(glb_fin_mdl_id) && (glb_fin_mdl_id %in% names(glb_models_lst))) {
warning("Final model same as user selected model")
glb_fin_mdl <- glb_models_lst[[glb_fin_mdl_id]]
} else
# if (nrow(glb_fitobs_df) + length(glbObsFitOutliers) == nrow(glb_trnobs_df))
if (!all(is.na(glb_newobs_df[, glb_rsp_var])))
{
warning("Final model same as glb_sel_mdl_id")
glb_fin_mdl_id <- paste0("Final.", glb_sel_mdl_id)
glb_fin_mdl <- glb_sel_mdl
glb_models_lst[[glb_fin_mdl_id]] <- glb_fin_mdl
} else {
# if (grepl("RFE", glb_sel_mdl_id) ||
# (!is.null(glb_mdl_ensemble) && grepl("RFE", glb_mdl_ensemble))) {
indep_vars <- myadjust_interaction_feats(subset(glb_feats_df,
!nzv & (exclude.as.feat != 1))[, "id"])
rfe_trn_results <-
myrun_rfe(glb_trnobs_df, indep_vars, glbRFESizes[["Final"]])
if (!isTRUE(all.equal(sort(predictors(rfe_trn_results)),
sort(predictors(rfe_fit_results))))) {
print("Diffs predictors(rfe_trn_results) vs. predictors(rfe_fit_results):")
print(setdiff(predictors(rfe_trn_results), predictors(rfe_fit_results)))
print("Diffs predictors(rfe_fit_results) vs. predictors(rfe_trn_results):")
print(setdiff(predictors(rfe_fit_results), predictors(rfe_trn_results)))
}
# }
if (grepl("Ensemble", glb_sel_mdl_id)) {
# Find which models are relevant
mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), importance > 5)
# Fit selected models on glb_trnobs_df
for (mdl_id in gsub(".prob", "",
gsub(glb_rsp_var_out, "", row.names(mdlimp_df), fixed = TRUE),
fixed = TRUE)) {
mdl_id_components <- unlist(strsplit(mdl_id, "[.]"))
mdlIdPfx <- paste0(c(head(mdl_id_components, -1), "Train"),
collapse = ".")
if (grepl("RFE\\.X\\.", mdlIdPfx))
mdlIndepVars <- myadjust_interaction_feats(myextract_actual_feats(
predictors(rfe_trn_results))) else
mdlIndepVars <- trim(unlist(
strsplit(glb_models_df[glb_models_df$id == mdl_id, "feats"], "[,]")))
ret_lst <-
myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = mdlIdPfx,
type = glb_model_type, tune.df = glb_tune_models_df,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = tail(mdl_id_components, 1))),
indep_vars = mdlIndepVars,
rsp_var = glb_rsp_var,
fit_df = glb_trnobs_df, OOB_df = NULL)
glb_trnobs_df <- glb_get_predictions(df = glb_trnobs_df,
mdl_id = tail(glb_models_df$id, 1),
rsp_var_out = glb_rsp_var_out,
prob_threshold_def =
subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
glb_newobs_df <- glb_get_predictions(df = glb_newobs_df,
mdl_id = tail(glb_models_df$id, 1),
rsp_var_out = glb_rsp_var_out,
prob_threshold_def =
subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
}
}
# "Final" model
if ((model_method <- glb_sel_mdl$method) == "custom")
# get actual method from the mdl_id
model_method <- tail(unlist(strsplit(glb_sel_mdl_id, "[.]")), 1)
if (grepl("Ensemble", glb_sel_mdl_id)) {
# Find which models are relevant
mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), importance > 5)
if (glb_is_classification && glb_is_binomial)
indep_vars_vctr <- gsub("(.*)\\.(.*)\\.prob", "\\1\\.Train\\.\\2\\.prob",
row.names(mdlimp_df)) else
indep_vars_vctr <- gsub("(.*)\\.(.*)", "\\1\\.Train\\.\\2",
row.names(mdlimp_df))
} else
if (grepl("RFE.X", glb_sel_mdl_id, fixed = TRUE)) {
indep_vars_vctr <- myextract_actual_feats(predictors(rfe_trn_results))
} else indep_vars_vctr <-
trim(unlist(strsplit(glb_models_df[glb_models_df$id ==
glb_sel_mdl_id
, "feats"], "[,]")))
# Discontinuing use of tune_finmdl_df;
# since final model needs to be cved on glb_trnobs_df
# tune_finmdl_df <- NULL
# if (nrow(glb_sel_mdl$bestTune) > 0) {
# for (param in names(glb_sel_mdl$bestTune)) {
# #print(sprintf("param: %s", param))
# if (glb_sel_mdl$bestTune[1, param] != "none")
# tune_finmdl_df <- rbind(tune_finmdl_df,
# data.frame(parameter=param,
# min=glb_sel_mdl$bestTune[1, param],
# max=glb_sel_mdl$bestTune[1, param],
# by=1)) # by val does not matter
# }
# }
# Sync with parameters in mydsutils.R
#stop(here"); glb_to_sav(); glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df
if (!is.null(glb_preproc_methods) &&
((match_pos <- regexpr(gsub(".", "\\.",
paste(glb_preproc_methods, collapse = "|"),
fixed = TRUE), glb_sel_mdl_id)) != -1))
ths_preProcess <- str_sub(glb_sel_mdl_id, match_pos,
match_pos + attr(match_pos, "match.length") - 1) else
ths_preProcess <- NULL
mdl_id_pfx <- ifelse(grepl("Ensemble", glb_sel_mdl_id),
"Final.Ensemble", "Final")
trnobs_df <- if (is.null(glbObsTrnOutliers[[mdl_id_pfx]])) glb_trnobs_df else
glb_trnobs_df[!(glb_trnobs_df[, glb_id_var] %in%
glbObsTrnOutliers[[mdl_id_pfx]]), ]
# Force fitting of Final.glm to identify outliers
method_vctr <-
unique(c("glm", tail(unlist(strsplit(glb_sel_mdl_id, "[.]")), 1)))
for (method in method_vctr) {
#source("caret_nominalTrainWorkflow.R")
# glmnet requires at least 2 indep vars
if ((length(indep_vars_vctr) == 1) && (method %in% "glmnet"))
next
ret_lst <-
myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = mdl_id_pfx,
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = method,
train.preProcess = ths_preProcess)),
indep_vars = indep_vars_vctr, rsp_var = glb_rsp_var,
fit_df = trnobs_df, OOB_df = NULL)
}
if ((length(method_vctr) == 1) || (method != "glm")) {
glb_fin_mdl <- glb_models_lst[[length(glb_models_lst)]]
glb_fin_mdl_id <- glb_models_df[length(glb_models_lst), "id"]
}
}## +(rfe) fit Fold1.Rep1 size: 71## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold1.Rep1 size: 71
## +(rfe) imp Fold1.Rep1
## -(rfe) imp Fold1.Rep1
## +(rfe) fit Fold1.Rep1 size: 70## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold1.Rep1 size: 70
## +(rfe) fit Fold1.Rep1 size: 69## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold1.Rep1 size: 69
## +(rfe) fit Fold1.Rep1 size: 66## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold1.Rep1 size: 66
## +(rfe) fit Fold1.Rep1 size: 64## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold1.Rep1 size: 64
## +(rfe) fit Fold1.Rep1 size: 32
## -(rfe) fit Fold1.Rep1 size: 32
## +(rfe) fit Fold1.Rep1 size: 16
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## +(rfe) fit Fold1.Rep1 size: 8
## -(rfe) fit Fold1.Rep1 size: 8
## +(rfe) fit Fold1.Rep1 size: 4
## -(rfe) fit Fold1.Rep1 size: 4
## +(rfe) fit Fold2.Rep1 size: 71## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold2.Rep1 size: 71
## +(rfe) imp Fold2.Rep1
## -(rfe) imp Fold2.Rep1
## +(rfe) fit Fold2.Rep1 size: 70## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold2.Rep1 size: 70
## +(rfe) fit Fold2.Rep1 size: 69## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold2.Rep1 size: 69
## +(rfe) fit Fold2.Rep1 size: 66## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold2.Rep1 size: 66
## +(rfe) fit Fold2.Rep1 size: 64## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold2.Rep1 size: 64
## +(rfe) fit Fold2.Rep1 size: 32
## -(rfe) fit Fold2.Rep1 size: 32
## +(rfe) fit Fold2.Rep1 size: 16
## -(rfe) fit Fold2.Rep1 size: 16
## +(rfe) fit Fold2.Rep1 size: 8
## -(rfe) fit Fold2.Rep1 size: 8
## +(rfe) fit Fold2.Rep1 size: 4
## -(rfe) fit Fold2.Rep1 size: 4
## +(rfe) fit Fold3.Rep1 size: 71## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold3.Rep1 size: 71
## +(rfe) imp Fold3.Rep1
## -(rfe) imp Fold3.Rep1
## +(rfe) fit Fold3.Rep1 size: 70## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold3.Rep1 size: 70
## +(rfe) fit Fold3.Rep1 size: 69## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold3.Rep1 size: 69
## +(rfe) fit Fold3.Rep1 size: 66## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold3.Rep1 size: 66
## +(rfe) fit Fold3.Rep1 size: 64## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold3.Rep1 size: 64
## +(rfe) fit Fold3.Rep1 size: 32
## -(rfe) fit Fold3.Rep1 size: 32
## +(rfe) fit Fold3.Rep1 size: 16
## -(rfe) fit Fold3.Rep1 size: 16
## +(rfe) fit Fold3.Rep1 size: 8
## -(rfe) fit Fold3.Rep1 size: 8
## +(rfe) fit Fold3.Rep1 size: 4
## -(rfe) fit Fold3.Rep1 size: 4
## +(rfe) fit Fold1.Rep2 size: 71## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold1.Rep2 size: 71
## +(rfe) imp Fold1.Rep2
## -(rfe) imp Fold1.Rep2
## +(rfe) fit Fold1.Rep2 size: 70## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold1.Rep2 size: 70
## +(rfe) fit Fold1.Rep2 size: 69## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold1.Rep2 size: 69
## +(rfe) fit Fold1.Rep2 size: 66## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold1.Rep2 size: 66
## +(rfe) fit Fold1.Rep2 size: 64## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold1.Rep2 size: 64
## +(rfe) fit Fold1.Rep2 size: 32
## -(rfe) fit Fold1.Rep2 size: 32
## +(rfe) fit Fold1.Rep2 size: 16
## -(rfe) fit Fold1.Rep2 size: 16
## +(rfe) fit Fold1.Rep2 size: 8
## -(rfe) fit Fold1.Rep2 size: 8
## +(rfe) fit Fold1.Rep2 size: 4
## -(rfe) fit Fold1.Rep2 size: 4
## +(rfe) fit Fold2.Rep2 size: 71## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold2.Rep2 size: 71
## +(rfe) imp Fold2.Rep2
## -(rfe) imp Fold2.Rep2
## +(rfe) fit Fold2.Rep2 size: 70## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold2.Rep2 size: 70
## +(rfe) fit Fold2.Rep2 size: 69## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold2.Rep2 size: 69
## +(rfe) fit Fold2.Rep2 size: 66## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold2.Rep2 size: 66
## +(rfe) fit Fold2.Rep2 size: 64## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold2.Rep2 size: 64
## +(rfe) fit Fold2.Rep2 size: 32
## -(rfe) fit Fold2.Rep2 size: 32
## +(rfe) fit Fold2.Rep2 size: 16
## -(rfe) fit Fold2.Rep2 size: 16
## +(rfe) fit Fold2.Rep2 size: 8
## -(rfe) fit Fold2.Rep2 size: 8
## +(rfe) fit Fold2.Rep2 size: 4
## -(rfe) fit Fold2.Rep2 size: 4
## +(rfe) fit Fold3.Rep2 size: 71## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold3.Rep2 size: 71
## +(rfe) imp Fold3.Rep2
## -(rfe) imp Fold3.Rep2
## +(rfe) fit Fold3.Rep2 size: 70## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold3.Rep2 size: 70
## +(rfe) fit Fold3.Rep2 size: 69## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold3.Rep2 size: 69
## +(rfe) fit Fold3.Rep2 size: 66## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold3.Rep2 size: 66
## +(rfe) fit Fold3.Rep2 size: 64## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold3.Rep2 size: 64
## +(rfe) fit Fold3.Rep2 size: 32
## -(rfe) fit Fold3.Rep2 size: 32
## +(rfe) fit Fold3.Rep2 size: 16
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## +(rfe) fit Fold3.Rep2 size: 8
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## +(rfe) fit Fold3.Rep2 size: 4
## -(rfe) fit Fold3.Rep2 size: 4
## +(rfe) fit Fold1.Rep3 size: 71## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold1.Rep3 size: 71
## +(rfe) imp Fold1.Rep3
## -(rfe) imp Fold1.Rep3
## +(rfe) fit Fold1.Rep3 size: 70## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold1.Rep3 size: 70
## +(rfe) fit Fold1.Rep3 size: 69## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold1.Rep3 size: 69
## +(rfe) fit Fold1.Rep3 size: 66## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold1.Rep3 size: 66
## +(rfe) fit Fold1.Rep3 size: 64## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold1.Rep3 size: 64
## +(rfe) fit Fold1.Rep3 size: 32
## -(rfe) fit Fold1.Rep3 size: 32
## +(rfe) fit Fold1.Rep3 size: 16
## -(rfe) fit Fold1.Rep3 size: 16
## +(rfe) fit Fold1.Rep3 size: 8
## -(rfe) fit Fold1.Rep3 size: 8
## +(rfe) fit Fold1.Rep3 size: 4
## -(rfe) fit Fold1.Rep3 size: 4
## +(rfe) fit Fold2.Rep3 size: 71## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold2.Rep3 size: 71
## +(rfe) imp Fold2.Rep3
## -(rfe) imp Fold2.Rep3
## +(rfe) fit Fold2.Rep3 size: 70## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold2.Rep3 size: 70
## +(rfe) fit Fold2.Rep3 size: 69## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold2.Rep3 size: 69
## +(rfe) fit Fold2.Rep3 size: 66## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold2.Rep3 size: 66
## +(rfe) fit Fold2.Rep3 size: 64## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold2.Rep3 size: 64
## +(rfe) fit Fold2.Rep3 size: 32
## -(rfe) fit Fold2.Rep3 size: 32
## +(rfe) fit Fold2.Rep3 size: 16
## -(rfe) fit Fold2.Rep3 size: 16
## +(rfe) fit Fold2.Rep3 size: 8
## -(rfe) fit Fold2.Rep3 size: 8
## +(rfe) fit Fold2.Rep3 size: 4
## -(rfe) fit Fold2.Rep3 size: 4
## +(rfe) fit Fold3.Rep3 size: 71## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold3.Rep3 size: 71
## +(rfe) imp Fold3.Rep3
## -(rfe) imp Fold3.Rep3
## +(rfe) fit Fold3.Rep3 size: 70## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold3.Rep3 size: 70
## +(rfe) fit Fold3.Rep3 size: 69## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold3.Rep3 size: 69
## +(rfe) fit Fold3.Rep3 size: 66## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold3.Rep3 size: 66
## +(rfe) fit Fold3.Rep3 size: 64## Warning in lda.default(x, grouping, ...): variables are collinear## -(rfe) fit Fold3.Rep3 size: 64
## +(rfe) fit Fold3.Rep3 size: 32
## -(rfe) fit Fold3.Rep3 size: 32
## +(rfe) fit Fold3.Rep3 size: 16
## -(rfe) fit Fold3.Rep3 size: 16
## +(rfe) fit Fold3.Rep3 size: 8
## -(rfe) fit Fold3.Rep3 size: 8
## +(rfe) fit Fold3.Rep3 size: 4
## -(rfe) fit Fold3.Rep3 size: 4## Warning in lda.default(x, grouping, ...): variables are collinear##
## Recursive feature selection
##
## Outer resampling method: Cross-Validated (3 fold, repeated 3 times)
##
## Resampling performance over subset size:
##
## Variables Accuracy Kappa AccuracySD KappaSD Selected
## 4 0.7825 0.5607 0.01203 0.02451
## 8 0.7878 0.5714 0.01198 0.02302
## 16 0.7980 0.5911 0.01559 0.03204
## 32 0.8088 0.6128 0.01467 0.02959
## 64 0.8117 0.6184 0.01552 0.03187
## 66 0.8130 0.6209 0.01412 0.02880
## 69 0.8146 0.6243 0.01580 0.03186
## 70 0.8250 0.6458 0.01063 0.02147 *
## 71 0.8250 0.6458 0.01063 0.02147
##
## The top 5 variables (out of 70):
## sprice.d20nexp, sprice.log10, sprice.root2, biddable, spdiff.cut.fctr(-1,0]
##
## [1] "sprice.d20nexp"
## [2] "sprice.log10"
## [3] "sprice.root2"
## [4] "biddable"
## [5] "spdiff.cut.fctr(-1,0]"
## [6] "spdiff.cut.fctr(1,10]"
## [7] "spdiff.cut.fctr(-10,-1]"
## [8] "spdiff.cut.fctr(10,100]"
## [9] "D.ratio.wrds.stop.n.wrds.n"
## [10] "cellular.fctr0:carrier.fctrNone"
## [11] "storage.fctr16"
## [12] "spdiff.cut.fctr(0,1]"
## [13] "condition.fctrFor parts or not working"
## [14] "prdl.my.fctriPad1"
## [15] "startprice.dcm2.is9"
## [16] "D.weight.sum.stem.stop.Ratio"
## [17] "prdl.my.fctriPad2"
## [18] "prdl.my.fctriPad3"
## [19] "color.fctrUnknown"
## [20] ".rnorm"
## [21] "prdl.my.fctriPadmini"
## [22] "prdl.my.fctriPadAir:.clusterid.fctr2"
## [23] "storage.fctr64"
## [24] "cellular.fctr1:carrier.fctrT-Mobile"
## [25] "prdl.my.fctriPadmini2"
## [26] "prdl.my.fctriPad1:.clusterid.fctr2"
## [27] "D.wrds.stop.n.log"
## [28] "cellular.fctr1:carrier.fctrSprint"
## [29] "prdl.my.fctriPad4:.clusterid.fctr2"
## [30] "prdl.my.fctriPadmini2:.clusterid.fctr2"
## [31] "prdl.my.fctriPad1:.clusterid.fctr3"
## [32] "cellular.fctr1:carrier.fctrVerizon"
## [33] "prdl.my.fctrUnknown:.clusterid.fctr2"
## [34] "storage.fctr32"
## [35] "prdl.my.fctrUnknown:.clusterid.fctr3"
## [36] "color.fctrSpace Gray"
## [37] "prdl.my.fctriPad3:.clusterid.fctr2"
## [38] "startprice.dcm1.is9"
## [39] "condition.fctrManufacturer refurbished"
## [40] "prdl.my.fctriPadAir"
## [41] "condition.fctrNew other (see details)"
## [42] "D.chrs.pnct11.n.log"
## [43] "prdl.my.fctriPadAir2"
## [44] "prdl.my.fctriPad4"
## [45] "cellular.fctr1:carrier.fctrUnknown"
## [46] "prdl.my.fctriPad4:.clusterid.fctr3"
## [47] "storage.fctrUnknown"
## [48] "startprice.dgt2.is9"
## [49] "color.fctrGold"
## [50] "prdl.my.fctriPadmini3"
## [51] "color.fctrWhite"
## [52] "D.ratio.weight.sum.wrds.n"
## [53] "condition.fctrSeller refurbished"
## [54] "cellular.fctrUnknown"
## [55] "cellular.fctrUnknown:carrier.fctrUnknown"
## [56] "spdiff.cut.fctr(100,1e+03]"
## [57] "cellular.fctr1"
## [58] "D.chrs.pnct13.n.log"
## [59] "D.weight.post.stop.sum"
## [60] "D.weight.post.stem.sum"
## [61] "D.weight.sum"
## [62] "D.wrds.n.log"
## [63] "D.chrs.uppr.n.log"
## [64] "D.wrds.unq.n.log"
## [65] "D.terms.post.stem.n.log"
## [66] "D.chrs.n.log"
## [67] "D.terms.post.stop.n.log"
## [68] "condition.fctrNew"
## [69] "startprice.dgt1.is9"
## [70] "spdiff.cut.fctr(-100,-10]"## [1] "Diffs predictors(rfe_trn_results) vs. predictors(rfe_fit_results):"
## [1] "prdl.my.fctriPadmini2:.clusterid.fctr2"
## [1] "Diffs predictors(rfe_fit_results) vs. predictors(rfe_trn_results):"
## character(0)
## [1] "fitting model: CSM.X.Train.gbm"
## [1] " indep_vars: spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting n.trees = 250, interaction.depth = 2, shrinkage = 0.1, n.minobsinnode = 10 on full training set## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 56: cellular.fctr1:carrier.fctrNone has no variation.## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 57: cellular.fctrUnknown:carrier.fctrNone has no variation.## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 58: cellular.fctr0:carrier.fctrOther has no variation.## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 60: cellular.fctrUnknown:carrier.fctrOther has no variation.## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 61: cellular.fctr0:carrier.fctrSprint has no variation.## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 63: cellular.fctrUnknown:carrier.fctrSprint has no variation.## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 64: cellular.fctr0:carrier.fctrT-Mobile has no variation.## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 66: cellular.fctrUnknown:carrier.fctrT-Mobile has no variation.## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 67: cellular.fctr0:carrier.fctrUnknown has no variation.## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 70: cellular.fctr0:carrier.fctrVerizon has no variation.## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 72: cellular.fctrUnknown:carrier.fctrVerizon has no variation.## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 75: prdl.my.fctriPad2:.clusterid.fctr2 has no variation.## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 79: prdl.my.fctriPadAir2:.clusterid.fctr2 has no variation.## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 80: prdl.my.fctriPadmini:.clusterid.fctr2 has no variation.## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 82: prdl.my.fctriPadmini3:.clusterid.fctr2 has no variation.## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 85: prdl.my.fctriPad2:.clusterid.fctr3 has no variation.## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 86: prdl.my.fctriPad3:.clusterid.fctr3 has no variation.## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 88: prdl.my.fctriPadAir:.clusterid.fctr3 has no variation.## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 89: prdl.my.fctriPadAir2:.clusterid.fctr3 has no variation.## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 90: prdl.my.fctriPadmini:.clusterid.fctr3 has no variation.## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 91: prdl.my.fctriPadmini2:.clusterid.fctr3 has no variation.## Warning in gbm.fit(x = structure(c(3.91202300542815, 4.61512051684126, 0, :
## variable 92: prdl.my.fctriPadmini3:.clusterid.fctr3 has no variation.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.3105 nan 0.1000 0.0353
## 2 1.2547 nan 0.1000 0.0282
## 3 1.2085 nan 0.1000 0.0232
## 4 1.1685 nan 0.1000 0.0188
## 5 1.1370 nan 0.1000 0.0159
## 6 1.1096 nan 0.1000 0.0132
## 7 1.0873 nan 0.1000 0.0109
## 8 1.0653 nan 0.1000 0.0103
## 9 1.0469 nan 0.1000 0.0093
## 10 1.0323 nan 0.1000 0.0067
## 20 0.9363 nan 0.1000 0.0027
## 40 0.8666 nan 0.1000 -0.0000
## 60 0.8239 nan 0.1000 -0.0002
## 80 0.7970 nan 0.1000 0.0001
## 100 0.7727 nan 0.1000 0.0002
## 120 0.7541 nan 0.1000 0.0001
## 140 0.7337 nan 0.1000 -0.0005
## 160 0.7174 nan 0.1000 -0.0007
## 180 0.7024 nan 0.1000 -0.0003
## 200 0.6902 nan 0.1000 -0.0008
## 220 0.6798 nan 0.1000 -0.0007
## 240 0.6684 nan 0.1000 -0.0008
## 250 0.6632 nan 0.1000 -0.0008## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdlIdPfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: n.trees## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdlIdPfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: shrinkage## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdlIdPfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: n.minobsinnode
## var
## biddable biddable
## sprice.log10 sprice.log10
## sprice.d20nexp sprice.d20nexp
## spdiff.cut.fctr(-10,-1] spdiff.cut.fctr(-10,-1]
## spdiff.cut.fctr(10,100] spdiff.cut.fctr(10,100]
## spdiff.cut.fctr(10,100]:biddable spdiff.cut.fctr(10,100]:biddable
## spdiff.cut.fctr(1,10] spdiff.cut.fctr(1,10]
## D.weight.sum.stem.stop.Ratio D.weight.sum.stem.stop.Ratio
## D.ratio.wrds.stop.n.wrds.n D.ratio.wrds.stop.n.wrds.n
## D.ratio.weight.sum.wrds.n D.ratio.weight.sum.wrds.n
## spdiff.cut.fctr(-100,-10]:biddable spdiff.cut.fctr(-100,-10]:biddable
## spdiff.cut.fctr(-100,-10] spdiff.cut.fctr(-100,-10]
## spdiff.cut.fctr(-1,0] spdiff.cut.fctr(-1,0]
## prdl.my.fctriPad1 prdl.my.fctriPad1
## spdiff.cut.fctr(100,1e+03]:biddable spdiff.cut.fctr(100,1e+03]:biddable
## prdl.my.fctriPadAir2 prdl.my.fctriPadAir2
## D.weight.post.stem.sum D.weight.post.stem.sum
## D.weight.post.stop.sum D.weight.post.stop.sum
## startprice.dcm1.is9 startprice.dcm1.is9
## D.terms.post.stop.n.log D.terms.post.stop.n.log
## D.chrs.n.log D.chrs.n.log
## spdiff.cut.fctr(100,1e+03] spdiff.cut.fctr(100,1e+03]
## D.terms.post.stem.n.log D.terms.post.stem.n.log
## cellular.fctrUnknown cellular.fctrUnknown
## prdl.my.fctriPad2 prdl.my.fctriPad2
## prdl.my.fctriPadAir prdl.my.fctriPadAir
## D.chrs.uppr.n.log D.chrs.uppr.n.log
## D.wrds.stop.n.log D.wrds.stop.n.log
## startprice.dcm2.is9 startprice.dcm2.is9
## spdiff.cut.fctr(0,1] spdiff.cut.fctr(0,1]
## startprice.dgt1.is9 startprice.dgt1.is9
## cellular.fctr1:carrier.fctrSprint cellular.fctr1:carrier.fctrSprint
## color.fctrUnknown color.fctrUnknown
## prdl.my.fctriPadmini prdl.my.fctriPadmini
## condition.fctrFor parts or not working condition.fctrFor parts or not working
## prdl.my.fctriPadmini2 prdl.my.fctriPadmini2
## D.wrds.n.log D.wrds.n.log
## prdl.my.fctriPad1:.clusterid.fctr3 prdl.my.fctriPad1:.clusterid.fctr3
## cellular.fctr1:carrier.fctrVerizon cellular.fctr1:carrier.fctrVerizon
## condition.fctrNew condition.fctrNew
## D.chrs.pnct13.n.log D.chrs.pnct13.n.log
## storage.fctr64 storage.fctr64
## prdl.my.fctrUnknown:.clusterid.fctr2 prdl.my.fctrUnknown:.clusterid.fctr2
## condition.fctrNew other (see details) condition.fctrNew other (see details)
## condition.fctrSeller refurbished condition.fctrSeller refurbished
## storage.fctrUnknown storage.fctrUnknown
## color.fctrWhite color.fctrWhite
## cellular.fctr1 cellular.fctr1
## cellular.fctr0:carrier.fctrNone cellular.fctr0:carrier.fctrNone
## D.chrs.pnct11.n.log D.chrs.pnct11.n.log
## color.fctrSpace Gray color.fctrSpace Gray
## startprice.dgt2.is9 startprice.dgt2.is9
## prdl.my.fctriPadAir:.clusterid.fctr2 prdl.my.fctriPadAir:.clusterid.fctr2
## storage.fctr32 storage.fctr32
## prdl.my.fctriPad1:.clusterid.fctr2 prdl.my.fctriPad1:.clusterid.fctr2
## storage.fctr16 storage.fctr16
## prdl.my.fctriPad3 prdl.my.fctriPad3
## prdl.my.fctriPad3:.clusterid.fctr2 prdl.my.fctriPad3:.clusterid.fctr2
## prdl.my.fctriPad4:.clusterid.fctr3 prdl.my.fctriPad4:.clusterid.fctr3
## cellular.fctr1:carrier.fctrT-Mobile cellular.fctr1:carrier.fctrT-Mobile
## condition.fctrManufacturer refurbished condition.fctrManufacturer refurbished
## D.weight.sum D.weight.sum
## D.wrds.unq.n.log D.wrds.unq.n.log
## color.fctrGold color.fctrGold
## prdl.my.fctriPad4 prdl.my.fctriPad4
## prdl.my.fctriPadmini3 prdl.my.fctriPadmini3
## sprice.root2 sprice.root2
## cellular.fctr1:carrier.fctrNone cellular.fctr1:carrier.fctrNone
## cellular.fctrUnknown:carrier.fctrNone cellular.fctrUnknown:carrier.fctrNone
## cellular.fctr0:carrier.fctrOther cellular.fctr0:carrier.fctrOther
## cellular.fctr1:carrier.fctrOther cellular.fctr1:carrier.fctrOther
## cellular.fctrUnknown:carrier.fctrOther cellular.fctrUnknown:carrier.fctrOther
## cellular.fctr0:carrier.fctrSprint cellular.fctr0:carrier.fctrSprint
## cellular.fctrUnknown:carrier.fctrSprint cellular.fctrUnknown:carrier.fctrSprint
## cellular.fctr0:carrier.fctrT-Mobile cellular.fctr0:carrier.fctrT-Mobile
## cellular.fctrUnknown:carrier.fctrT-Mobile cellular.fctrUnknown:carrier.fctrT-Mobile
## cellular.fctr0:carrier.fctrUnknown cellular.fctr0:carrier.fctrUnknown
## cellular.fctr1:carrier.fctrUnknown cellular.fctr1:carrier.fctrUnknown
## cellular.fctrUnknown:carrier.fctrUnknown cellular.fctrUnknown:carrier.fctrUnknown
## cellular.fctr0:carrier.fctrVerizon cellular.fctr0:carrier.fctrVerizon
## cellular.fctrUnknown:carrier.fctrVerizon cellular.fctrUnknown:carrier.fctrVerizon
## prdl.my.fctriPad2:.clusterid.fctr2 prdl.my.fctriPad2:.clusterid.fctr2
## prdl.my.fctriPad4:.clusterid.fctr2 prdl.my.fctriPad4:.clusterid.fctr2
## prdl.my.fctriPadAir2:.clusterid.fctr2 prdl.my.fctriPadAir2:.clusterid.fctr2
## prdl.my.fctriPadmini:.clusterid.fctr2 prdl.my.fctriPadmini:.clusterid.fctr2
## prdl.my.fctriPadmini2:.clusterid.fctr2 prdl.my.fctriPadmini2:.clusterid.fctr2
## prdl.my.fctriPadmini3:.clusterid.fctr2 prdl.my.fctriPadmini3:.clusterid.fctr2
## prdl.my.fctrUnknown:.clusterid.fctr3 prdl.my.fctrUnknown:.clusterid.fctr3
## prdl.my.fctriPad2:.clusterid.fctr3 prdl.my.fctriPad2:.clusterid.fctr3
## prdl.my.fctriPad3:.clusterid.fctr3 prdl.my.fctriPad3:.clusterid.fctr3
## prdl.my.fctriPadAir:.clusterid.fctr3 prdl.my.fctriPadAir:.clusterid.fctr3
## prdl.my.fctriPadAir2:.clusterid.fctr3 prdl.my.fctriPadAir2:.clusterid.fctr3
## prdl.my.fctriPadmini:.clusterid.fctr3 prdl.my.fctriPadmini:.clusterid.fctr3
## prdl.my.fctriPadmini2:.clusterid.fctr3 prdl.my.fctriPadmini2:.clusterid.fctr3
## prdl.my.fctriPadmini3:.clusterid.fctr3 prdl.my.fctriPadmini3:.clusterid.fctr3
## spdiff.cut.fctr(-10,-1]:biddable spdiff.cut.fctr(-10,-1]:biddable
## spdiff.cut.fctr(-1,0]:biddable spdiff.cut.fctr(-1,0]:biddable
## spdiff.cut.fctr(0,1]:biddable spdiff.cut.fctr(0,1]:biddable
## spdiff.cut.fctr(1,10]:biddable spdiff.cut.fctr(1,10]:biddable
## rel.inf
## biddable 34.72685273
## sprice.log10 16.23512409
## sprice.d20nexp 8.49439543
## spdiff.cut.fctr(-10,-1] 3.50254730
## spdiff.cut.fctr(10,100] 2.74112330
## spdiff.cut.fctr(10,100]:biddable 2.61180244
## spdiff.cut.fctr(1,10] 2.44756503
## D.weight.sum.stem.stop.Ratio 1.94601173
## D.ratio.wrds.stop.n.wrds.n 1.85774254
## D.ratio.weight.sum.wrds.n 1.81559469
## spdiff.cut.fctr(-100,-10]:biddable 1.76251208
## spdiff.cut.fctr(-100,-10] 1.66938154
## spdiff.cut.fctr(-1,0] 1.38171158
## prdl.my.fctriPad1 1.26232840
## spdiff.cut.fctr(100,1e+03]:biddable 1.12731640
## prdl.my.fctriPadAir2 1.11604007
## D.weight.post.stem.sum 0.93913524
## D.weight.post.stop.sum 0.84671791
## startprice.dcm1.is9 0.79000270
## D.terms.post.stop.n.log 0.77667525
## D.chrs.n.log 0.76356151
## spdiff.cut.fctr(100,1e+03] 0.71163762
## D.terms.post.stem.n.log 0.69891167
## cellular.fctrUnknown 0.69770274
## prdl.my.fctriPad2 0.67829704
## prdl.my.fctriPadAir 0.64835911
## D.chrs.uppr.n.log 0.63959430
## D.wrds.stop.n.log 0.59627411
## startprice.dcm2.is9 0.53729458
## spdiff.cut.fctr(0,1] 0.42533741
## startprice.dgt1.is9 0.41691593
## cellular.fctr1:carrier.fctrSprint 0.37968187
## color.fctrUnknown 0.37664058
## prdl.my.fctriPadmini 0.34995699
## condition.fctrFor parts or not working 0.34791971
## prdl.my.fctriPadmini2 0.30646888
## D.wrds.n.log 0.29727660
## prdl.my.fctriPad1:.clusterid.fctr3 0.27818269
## cellular.fctr1:carrier.fctrVerizon 0.26826420
## condition.fctrNew 0.25431043
## D.chrs.pnct13.n.log 0.24072298
## storage.fctr64 0.22090428
## prdl.my.fctrUnknown:.clusterid.fctr2 0.16949122
## condition.fctrNew other (see details) 0.16878370
## condition.fctrSeller refurbished 0.16126313
## storage.fctrUnknown 0.14162078
## color.fctrWhite 0.13384454
## cellular.fctr1 0.13368950
## cellular.fctr0:carrier.fctrNone 0.12986623
## D.chrs.pnct11.n.log 0.10276445
## color.fctrSpace Gray 0.09786977
## startprice.dgt2.is9 0.09310402
## prdl.my.fctriPadAir:.clusterid.fctr2 0.08470155
## storage.fctr32 0.07661380
## prdl.my.fctriPad1:.clusterid.fctr2 0.06592890
## storage.fctr16 0.05861855
## prdl.my.fctriPad3 0.04807145
## prdl.my.fctriPad3:.clusterid.fctr2 0.04411656
## prdl.my.fctriPad4:.clusterid.fctr3 0.03990263
## cellular.fctr1:carrier.fctrT-Mobile 0.03464323
## condition.fctrManufacturer refurbished 0.03031028
## D.weight.sum 0.00000000
## D.wrds.unq.n.log 0.00000000
## color.fctrGold 0.00000000
## prdl.my.fctriPad4 0.00000000
## prdl.my.fctriPadmini3 0.00000000
## sprice.root2 0.00000000
## cellular.fctr1:carrier.fctrNone 0.00000000
## cellular.fctrUnknown:carrier.fctrNone 0.00000000
## cellular.fctr0:carrier.fctrOther 0.00000000
## cellular.fctr1:carrier.fctrOther 0.00000000
## cellular.fctrUnknown:carrier.fctrOther 0.00000000
## cellular.fctr0:carrier.fctrSprint 0.00000000
## cellular.fctrUnknown:carrier.fctrSprint 0.00000000
## cellular.fctr0:carrier.fctrT-Mobile 0.00000000
## cellular.fctrUnknown:carrier.fctrT-Mobile 0.00000000
## cellular.fctr0:carrier.fctrUnknown 0.00000000
## cellular.fctr1:carrier.fctrUnknown 0.00000000
## cellular.fctrUnknown:carrier.fctrUnknown 0.00000000
## cellular.fctr0:carrier.fctrVerizon 0.00000000
## cellular.fctrUnknown:carrier.fctrVerizon 0.00000000
## prdl.my.fctriPad2:.clusterid.fctr2 0.00000000
## prdl.my.fctriPad4:.clusterid.fctr2 0.00000000
## prdl.my.fctriPadAir2:.clusterid.fctr2 0.00000000
## prdl.my.fctriPadmini:.clusterid.fctr2 0.00000000
## prdl.my.fctriPadmini2:.clusterid.fctr2 0.00000000
## prdl.my.fctriPadmini3:.clusterid.fctr2 0.00000000
## prdl.my.fctrUnknown:.clusterid.fctr3 0.00000000
## prdl.my.fctriPad2:.clusterid.fctr3 0.00000000
## prdl.my.fctriPad3:.clusterid.fctr3 0.00000000
## prdl.my.fctriPadAir:.clusterid.fctr3 0.00000000
## prdl.my.fctriPadAir2:.clusterid.fctr3 0.00000000
## prdl.my.fctriPadmini:.clusterid.fctr3 0.00000000
## prdl.my.fctriPadmini2:.clusterid.fctr3 0.00000000
## prdl.my.fctriPadmini3:.clusterid.fctr3 0.00000000
## spdiff.cut.fctr(-10,-1]:biddable 0.00000000
## spdiff.cut.fctr(-1,0]:biddable 0.00000000
## spdiff.cut.fctr(0,1]:biddable 0.00000000
## spdiff.cut.fctr(1,10]:biddable 0.00000000
## sold.fctr sold.fctr.predict.CSM.X.Train.gbm.N
## 1 N 858
## 2 Y 119
## sold.fctr.predict.CSM.X.Train.gbm.Y
## 1 137
## 2 736
## Prediction
## Reference N Y
## N 858 137
## Y 119 736
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.616216e-01 7.220608e-01 8.450434e-01 8.770437e-01 5.378378e-01
## AccuracyPValue McnemarPValue
## 2.270830e-194 2.880088e-01
## id
## 1 CSM.X.Train.gbm
## feats
## 1 spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 23.516 1.911
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8571219 0.9165829 0.7976608 0.9367586
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.8518519 0.8198219
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8450434 0.8770437 0.6344694
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.008737428 0.01819162## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.3## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.3
## [1] "fitting model: CSM.X.Train.avNNet"
## [1] " indep_vars: spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting size = 1, decay = 0.1, bag = FALSE on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdlIdPfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: size## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdlIdPfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: decay## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdlIdPfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: bag
## Length Class Mode
## model 5 -none- list
## repeats 1 -none- numeric
## bag 1 -none- logical
## names 99 -none- character
## terms 3 terms call
## coefnames 99 -none- character
## xlevels 0 -none- list
## xNames 99 -none- character
## problemType 1 -none- character
## tuneValue 3 data.frame list
## obsLevels 2 -none- character
## sold.fctr sold.fctr.predict.CSM.X.Train.avNNet.N
## 1 N 903
## 2 Y 165
## sold.fctr.predict.CSM.X.Train.avNNet.Y
## 1 92
## 2 690
## Prediction
## Reference N Y
## N 903 92
## Y 165 690
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.610811e-01 7.188732e-01 8.444780e-01 8.765298e-01 5.378378e-01
## AccuracyPValue McnemarPValue
## 1.211532e-193 7.081083e-06
## id
## 1 CSM.X.Train.avNNet
## feats
## 1 spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 191.207 1.692
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8572776 0.9075377 0.8070175 0.9140098
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.8430055 0.8232434
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.844478 0.8765298 0.6424646
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.009235932 0.01882766## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.3## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.3
## [1] "fitting model: CSM.X.Interact.Train.glmnet"
## [1] " indep_vars: .rnorm,D.weight.sum,D.weight.post.stem.sum,D.chrs.pnct13.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,startprice.dcm2.is9,sprice.root2,sprice.log10,startprice.dcm1.is9,D.weight.sum.stem.stop.Ratio,spdiff.cut.fctr*biddable,spdiff.cut.fctr*storage.fctr,spdiff.cut.fctr*prdl.my.fctr,spdiff.cut.fctr*cellular.fctr,spdiff.cut.fctr*D.chrs.pnct11.n.log,spdiff.cut.fctr*condition.fctr,spdiff.cut.fctr*color.fctr,spdiff.cut.fctr*sprice.d20nexp,spdiff.cut.fctr*startprice.dgt2.is9,spdiff.cut.fctr*D.wrds.n.log,spdiff.cut.fctr*D.chrs.n.log,spdiff.cut.fctr*D.chrs.uppr.n.log,spdiff.cut.fctr*D.terms.post.stem.n.log,spdiff.cut.fctr*D.terms.post.stop.n.log,spdiff.cut.fctr*D.wrds.stop.n.log,spdiff.cut.fctr*D.wrds.unq.n.log,spdiff.cut.fctr*startprice.dgt1.is9,spdiff.cut.fctr*D.weight.post.stop.sum,spdiff.cut.fctr*cellular.fctr:carrier.fctr,spdiff.cut.fctr*prdl.my.fctr:.clusterid.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.325, lambda = 0.0254 on full training set
## Length Class Mode
## a0 100 -none- numeric
## beta 61800 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 618 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## 4.352139e-01
## spdiff.cut.fctr(-10,-1]
## 8.938407e-01
## spdiff.cut.fctr(-1,0]
## 1.161460e+00
## spdiff.cut.fctr(0,1]
## 7.394212e-01
## spdiff.cut.fctr(1,10]
## 6.298744e-01
## spdiff.cut.fctr(10,100]
## 4.406540e-01
## spdiff.cut.fctr(100,1e+03]
## -2.051795e-01
## D.terms.post.stem.n.log
## -7.739751e-03
## D.terms.post.stop.n.log
## -1.304662e-02
## D.wrds.unq.n.log
## -7.026868e-03
## biddable
## 1.010124e+00
## cellular.fctrUnknown
## -4.359210e-02
## condition.fctrSeller refurbished
## -7.531953e-02
## prdl.my.fctriPadAir2
## 2.183546e-01
## sprice.d20nexp
## 1.783147e-01
## sprice.log10
## -1.552374e-01
## sprice.root2
## -7.518387e-02
## startprice.dcm1.is9
## -2.009063e-01
## spdiff.cut.fctr(-1,0]:D.chrs.n.log
## 2.989490e-03
## spdiff.cut.fctr(-10,-1]:D.chrs.pnct11.n.log
## 1.681953e-01
## spdiff.cut.fctr(10,100]:D.chrs.pnct11.n.log
## -1.418104e-02
## spdiff.cut.fctr(-1,0]:D.chrs.uppr.n.log
## 3.000779e-03
## spdiff.cut.fctr(-1,0]:D.terms.post.stem.n.log
## 3.244840e-04
## spdiff.cut.fctr(-1,0]:D.weight.post.stop.sum
## 7.320624e-03
## spdiff.cut.fctr(-1,0]:D.wrds.n.log
## 1.605493e-02
## spdiff.cut.fctr(-100,-10]:D.wrds.stop.n.log
## 1.721571e-01
## spdiff.cut.fctr(1,10]:D.wrds.stop.n.log
## 9.632826e-02
## spdiff.cut.fctr(-100,-10]:biddable
## 7.506323e-01
## spdiff.cut.fctr(-10,-1]:biddable
## 3.112216e-01
## spdiff.cut.fctr(-1,0]:biddable
## 4.163382e-01
## spdiff.cut.fctr(0,1]:biddable
## 4.500445e-01
## spdiff.cut.fctr(1,10]:biddable
## 8.654216e-01
## spdiff.cut.fctr(10,100]:biddable
## 1.024730e+00
## spdiff.cut.fctr(100,1e+03]:biddable
## 2.143616e+00
## spdiff.cut.fctr(-1,0]:cellular.fctr1
## 1.228412e-01
## spdiff.cut.fctr(0,1]:cellular.fctr1
## 3.903318e-01
## spdiff.cut.fctr(1,10]:cellular.fctr1
## 3.712176e-02
## spdiff.cut.fctr(10,100]:cellular.fctr1
## 3.253101e-01
## spdiff.cut.fctr(100,1e+03]:cellular.fctrUnknown
## -7.485190e-02
## cellular.fctrUnknown:carrier.fctrUnknown
## -3.874953e-02
## spdiff.cut.fctr(-10,-1]:color.fctrGold
## 3.421757e-01
## spdiff.cut.fctr(-1,0]:color.fctrGold
## -2.690429e-01
## spdiff.cut.fctr(1,10]:color.fctrGold
## 5.010574e-01
## spdiff.cut.fctr(-100,-10]:color.fctrSpace Gray
## 6.526845e-02
## spdiff.cut.fctr(-1,0]:color.fctrUnknown
## 1.308989e-01
## spdiff.cut.fctr(10,100]:color.fctrUnknown
## 1.040129e-01
## spdiff.cut.fctr(10,100]:color.fctrWhite
## 4.196057e-02
## spdiff.cut.fctr(-100,-10]:condition.fctrFor parts or not working
## -3.902676e-01
## spdiff.cut.fctr(-100,-10]:condition.fctrNew
## 1.550066e-01
## spdiff.cut.fctr(1,10]:condition.fctrNew other (see details)
## 1.904829e+00
## spdiff.cut.fctr(10,100]:condition.fctrNew other (see details)
## 1.195434e+00
## spdiff.cut.fctr(100,1e+03]:condition.fctrNew other (see details)
## 9.387914e-01
## spdiff.cut.fctr(-100,-10]:condition.fctrSeller refurbished
## -3.980802e-01
## spdiff.cut.fctr(-10,-1]:condition.fctrSeller refurbished
## -3.721203e-01
## spdiff.cut.fctr(-100,-10]:prdl.my.fctriPad1
## -8.217347e-01
## spdiff.cut.fctr(-10,-1]:prdl.my.fctriPad1
## 1.019699e-01
## spdiff.cut.fctr(0,1]:prdl.my.fctriPad2
## 9.088240e-01
## spdiff.cut.fctr(1,10]:prdl.my.fctriPad2
## 2.491114e-01
## spdiff.cut.fctr(10,100]:prdl.my.fctriPad2
## -6.280514e-01
## spdiff.cut.fctr(100,1e+03]:prdl.my.fctriPad3
## -2.294652e+00
## spdiff.cut.fctr(-10,-1]:prdl.my.fctriPad4
## 7.246006e-02
## spdiff.cut.fctr(10,100]:prdl.my.fctriPad4
## 3.163869e-01
## spdiff.cut.fctr(-100,-10]:prdl.my.fctriPadAir
## 2.738805e-01
## spdiff.cut.fctr(-10,-1]:prdl.my.fctriPadAir
## 1.773844e+00
## spdiff.cut.fctr(10,100]:prdl.my.fctriPadAir
## 1.068666e+00
## spdiff.cut.fctr(-100,-10]:prdl.my.fctriPadAir2
## 2.203094e-01
## spdiff.cut.fctr(10,100]:prdl.my.fctriPadAir2
## 6.920297e-01
## spdiff.cut.fctr(1,10]:prdl.my.fctriPadmini2
## -3.180343e-01
## spdiff.cut.fctr(10,100]:prdl.my.fctriPadmini2
## 1.094421e+00
## spdiff.cut.fctr(-10,-1]:prdl.my.fctriPadmini3
## -4.961524e-05
## prdl.my.fctrUnknown:.clusterid.fctr2
## 8.814495e-02
## prdl.my.fctriPad1:.clusterid.fctr3
## -2.744950e-01
## prdl.my.fctriPad4:.clusterid.fctr3
## -1.007015e+00
## spdiff.cut.fctr(-100,-10]:sprice.d20nexp
## 1.027509e+00
## spdiff.cut.fctr(-10,-1]:sprice.d20nexp
## 2.099484e-01
## spdiff.cut.fctr(-100,-10]:startprice.dgt1.is9
## 7.151242e-02
## spdiff.cut.fctr(-10,-1]:startprice.dgt1.is9
## 1.785883e-01
## spdiff.cut.fctr(1,10]:startprice.dgt1.is9
## 1.887708e-02
## spdiff.cut.fctr(10,100]:startprice.dgt1.is9
## 1.247234e-02
## spdiff.cut.fctr(100,1e+03]:startprice.dgt1.is9
## -4.527587e-01
## spdiff.cut.fctr(-1,0]:startprice.dgt2.is9
## 1.320826e+00
## spdiff.cut.fctr(10,100]:startprice.dgt2.is9
## 4.140114e-01
## spdiff.cut.fctr(-1,0]:storage.fctr16
## 2.191025e-01
## spdiff.cut.fctr(0,1]:storage.fctr16
## 3.712821e-01
## spdiff.cut.fctr(1,10]:storage.fctr16
## 2.954584e-01
## spdiff.cut.fctr(10,100]:storage.fctr16
## 1.515950e-02
## spdiff.cut.fctr(100,1e+03]:storage.fctr16
## -4.225205e-01
## spdiff.cut.fctr(-100,-10]:storage.fctr64
## 4.464197e-01
## spdiff.cut.fctr(-10,-1]:storage.fctr64
## 3.315198e-01
## spdiff.cut.fctr(-1,0]:storage.fctr64
## 1.668681e-01
## spdiff.cut.fctr(-10,-1]:storage.fctrUnknown
## 9.360191e-01
## spdiff.cut.fctr(100,1e+03]:storage.fctrUnknown
## -3.390300e-02
## spdiff.cut.fctr(-10,-1]:cellular.fctr0:carrier.fctrNone
## 3.144616e-01
## spdiff.cut.fctr(-1,0]:cellular.fctr0:carrier.fctrNone
## 2.841618e-01
## spdiff.cut.fctr(10,100]:cellular.fctr0:carrier.fctrNone
## 5.048789e-04
## spdiff.cut.fctr(-100,-10]:cellular.fctr1:carrier.fctrSprint
## 7.481110e-01
## spdiff.cut.fctr(-100,-10]:cellular.fctr1:carrier.fctrT-Mobile
## 7.194765e-02
## spdiff.cut.fctr(-10,-1]:cellular.fctr1:carrier.fctrT-Mobile
## 1.034730e+00
## spdiff.cut.fctr(10,100]:cellular.fctr1:carrier.fctrT-Mobile
## 5.531298e-01
## spdiff.cut.fctr(100,1e+03]:cellular.fctr1:carrier.fctrUnknown
## 9.544992e-02
## spdiff.cut.fctr(100,1e+03]:cellular.fctrUnknown:carrier.fctrUnknown
## -8.554153e-02
## spdiff.cut.fctr(-100,-10]:cellular.fctr1:carrier.fctrVerizon
## 5.337052e-02
## spdiff.cut.fctr(-100,-10]:prdl.my.fctrUnknown:.clusterid.fctr2
## 4.071145e-02
## spdiff.cut.fctr(100,1e+03]:prdl.my.fctrUnknown:.clusterid.fctr2
## -1.789496e-01
## spdiff.cut.fctr(-100,-10]:prdl.my.fctriPad1:.clusterid.fctr2
## -2.553988e-01
## spdiff.cut.fctr(1,10]:prdl.my.fctriPad1:.clusterid.fctr2
## -2.565532e-01
## spdiff.cut.fctr(-100,-10]:prdl.my.fctriPad3:.clusterid.fctr2
## -1.048248e-01
## spdiff.cut.fctr(-100,-10]:prdl.my.fctriPad4:.clusterid.fctr2
## 2.160957e-01
## spdiff.cut.fctr(-10,-1]:prdl.my.fctriPadmini2:.clusterid.fctr2
## -1.042772e-01
## spdiff.cut.fctr(1,10]:prdl.my.fctriPadmini2:.clusterid.fctr2
## -3.918943e-01
## spdiff.cut.fctr(10,100]:prdl.my.fctriPadmini2:.clusterid.fctr2
## 3.408233e-01
## spdiff.cut.fctr(-100,-10]:prdl.my.fctrUnknown:.clusterid.fctr3
## -6.675256e-01
## spdiff.cut.fctr(1,10]:prdl.my.fctrUnknown:.clusterid.fctr3
## 2.037546e+00
## spdiff.cut.fctr(10,100]:prdl.my.fctrUnknown:.clusterid.fctr3
## -5.378027e-01
## spdiff.cut.fctr(100,1e+03]:prdl.my.fctrUnknown:.clusterid.fctr3
## -2.560218e-01
## spdiff.cut.fctr(-10,-1]:prdl.my.fctriPad1:.clusterid.fctr3
## -3.082521e-01
## spdiff.cut.fctr(1,10]:prdl.my.fctriPad1:.clusterid.fctr3
## -4.298746e-01
## spdiff.cut.fctr(-10,-1]:prdl.my.fctriPad4:.clusterid.fctr3
## -1.948086e-01
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## 0.4439883416
## spdiff.cut.fctr(-10,-1]
## 0.9220797767
## spdiff.cut.fctr(-1,0]
## 1.1925838176
## spdiff.cut.fctr(0,1]
## 0.7611534209
## spdiff.cut.fctr(1,10]
## 0.6477286968
## spdiff.cut.fctr(10,100]
## 0.4541029050
## spdiff.cut.fctr(100,1e+03]
## -0.2263312377
## D.terms.post.stem.n.log
## -0.0091025524
## D.terms.post.stop.n.log
## -0.0149751997
## D.wrds.unq.n.log
## -0.0090287164
## biddable
## 1.0164934373
## cellular.fctrUnknown
## -0.0494902988
## condition.fctrSeller refurbished
## -0.0814694274
## prdl.my.fctriPadAir2
## 0.2345919348
## prdl.my.fctriPadmini
## -0.0088927713
## sprice.d20nexp
## 0.1617379599
## sprice.log10
## -0.1579628619
## sprice.root2
## -0.0771110119
## startprice.dcm1.is9
## -0.2325637348
## spdiff.cut.fctr(-1,0]:D.chrs.n.log
## 0.0030731256
## spdiff.cut.fctr(-10,-1]:D.chrs.pnct11.n.log
## 0.2115917342
## spdiff.cut.fctr(10,100]:D.chrs.pnct11.n.log
## -0.0419311672
## spdiff.cut.fctr(-1,0]:D.chrs.uppr.n.log
## 0.0032701580
## spdiff.cut.fctr(-1,0]:D.terms.post.stem.n.log
## 0.0024763045
## spdiff.cut.fctr(1,10]:D.terms.post.stem.n.log
## 0.0050273992
## spdiff.cut.fctr(-1,0]:D.terms.post.stop.n.log
## 0.0014480588
## spdiff.cut.fctr(-1,0]:D.weight.post.stop.sum
## 0.0065940963
## spdiff.cut.fctr(-100,-10]:D.wrds.n.log
## 0.0001497788
## spdiff.cut.fctr(-1,0]:D.wrds.n.log
## 0.0177745946
## spdiff.cut.fctr(-100,-10]:D.wrds.stop.n.log
## 0.2076561101
## spdiff.cut.fctr(1,10]:D.wrds.stop.n.log
## 0.1216792884
## spdiff.cut.fctr(-1,0]:D.wrds.unq.n.log
## 0.0005544612
## spdiff.cut.fctr(1,10]:D.wrds.unq.n.log
## 0.0011712459
## spdiff.cut.fctr(-100,-10]:biddable
## 0.7717474748
## spdiff.cut.fctr(-10,-1]:biddable
## 0.3033969839
## spdiff.cut.fctr(-1,0]:biddable
## 0.4465095905
## spdiff.cut.fctr(0,1]:biddable
## 0.4916981291
## spdiff.cut.fctr(1,10]:biddable
## 0.8946656131
## spdiff.cut.fctr(10,100]:biddable
## 1.0397640611
## spdiff.cut.fctr(100,1e+03]:biddable
## 2.2797883782
## spdiff.cut.fctr(-1,0]:cellular.fctr1
## 0.1509655418
## spdiff.cut.fctr(0,1]:cellular.fctr1
## 0.4529868802
## spdiff.cut.fctr(1,10]:cellular.fctr1
## 0.0511598266
## spdiff.cut.fctr(10,100]:cellular.fctr1
## 0.3609963106
## spdiff.cut.fctr(100,1e+03]:cellular.fctrUnknown
## -0.0571216220
## cellular.fctrUnknown:carrier.fctrUnknown
## -0.0457180645
## spdiff.cut.fctr(-10,-1]:color.fctrGold
## 0.4084342900
## spdiff.cut.fctr(-1,0]:color.fctrGold
## -0.4345070693
## spdiff.cut.fctr(1,10]:color.fctrGold
## 0.6277572029
## spdiff.cut.fctr(-100,-10]:color.fctrSpace Gray
## 0.0833467888
## spdiff.cut.fctr(-1,0]:color.fctrUnknown
## 0.1534925078
## spdiff.cut.fctr(10,100]:color.fctrUnknown
## 0.1211627346
## spdiff.cut.fctr(10,100]:color.fctrWhite
## 0.0620813496
## spdiff.cut.fctr(-100,-10]:condition.fctrFor parts or not working
## -0.4252316003
## spdiff.cut.fctr(-10,-1]:condition.fctrManufacturer refurbished
## -0.0828011871
## spdiff.cut.fctr(-100,-10]:condition.fctrNew
## 0.1901435886
## spdiff.cut.fctr(1,10]:condition.fctrNew other (see details)
## 2.0261429213
## spdiff.cut.fctr(10,100]:condition.fctrNew other (see details)
## 1.3067697543
## spdiff.cut.fctr(100,1e+03]:condition.fctrNew other (see details)
## 1.0604131447
## spdiff.cut.fctr(-100,-10]:condition.fctrSeller refurbished
## -0.4300310587
## spdiff.cut.fctr(-10,-1]:condition.fctrSeller refurbished
## -0.4915650018
## spdiff.cut.fctr(-100,-10]:prdl.my.fctriPad1
## -0.8611879803
## spdiff.cut.fctr(-10,-1]:prdl.my.fctriPad1
## 0.1200145700
## spdiff.cut.fctr(0,1]:prdl.my.fctriPad2
## 1.0156274392
## spdiff.cut.fctr(1,10]:prdl.my.fctriPad2
## 0.2684220800
## spdiff.cut.fctr(10,100]:prdl.my.fctriPad2
## -0.6674312871
## spdiff.cut.fctr(1,10]:prdl.my.fctriPad3
## 0.0082634457
## spdiff.cut.fctr(100,1e+03]:prdl.my.fctriPad3
## -2.5131730102
## spdiff.cut.fctr(-10,-1]:prdl.my.fctriPad4
## 0.1343738472
## spdiff.cut.fctr(10,100]:prdl.my.fctriPad4
## 0.3348566294
## spdiff.cut.fctr(-100,-10]:prdl.my.fctriPadAir
## 0.2972972239
## spdiff.cut.fctr(-10,-1]:prdl.my.fctriPadAir
## 1.9003602700
## spdiff.cut.fctr(10,100]:prdl.my.fctriPadAir
## 1.1224858349
## spdiff.cut.fctr(-100,-10]:prdl.my.fctriPadAir2
## 0.2340807762
## spdiff.cut.fctr(10,100]:prdl.my.fctriPadAir2
## 0.7049217511
## spdiff.cut.fctr(1,10]:prdl.my.fctriPadmini2
## -0.3738242671
## spdiff.cut.fctr(10,100]:prdl.my.fctriPadmini2
## 1.1128416225
## spdiff.cut.fctr(-10,-1]:prdl.my.fctriPadmini3
## -0.0794541535
## spdiff.cut.fctr(1,10]:prdl.my.fctriPadmini3
## 0.0257412333
## prdl.my.fctrUnknown:.clusterid.fctr2
## 0.1723571923
## prdl.my.fctriPad1:.clusterid.fctr3
## -0.3020777327
## prdl.my.fctriPad4:.clusterid.fctr3
## -1.0702792095
## spdiff.cut.fctr(-100,-10]:sprice.d20nexp
## 1.3702210519
## spdiff.cut.fctr(-10,-1]:sprice.d20nexp
## 0.3477035516
## spdiff.cut.fctr(-1,0]:sprice.d20nexp
## 0.0166298124
## spdiff.cut.fctr(-100,-10]:startprice.dgt1.is9
## 0.1089497913
## spdiff.cut.fctr(-10,-1]:startprice.dgt1.is9
## 0.2223909108
## spdiff.cut.fctr(1,10]:startprice.dgt1.is9
## 0.0396795348
## spdiff.cut.fctr(10,100]:startprice.dgt1.is9
## 0.0398603867
## spdiff.cut.fctr(100,1e+03]:startprice.dgt1.is9
## -0.4883080915
## spdiff.cut.fctr(-1,0]:startprice.dgt2.is9
## 1.4180711088
## spdiff.cut.fctr(10,100]:startprice.dgt2.is9
## 0.4277641354
## spdiff.cut.fctr(-1,0]:storage.fctr16
## 0.2465247377
## spdiff.cut.fctr(0,1]:storage.fctr16
## 0.4280476768
## spdiff.cut.fctr(1,10]:storage.fctr16
## 0.3097217917
## spdiff.cut.fctr(10,100]:storage.fctr16
## 0.0174531776
## spdiff.cut.fctr(100,1e+03]:storage.fctr16
## -0.4688847124
## spdiff.cut.fctr(-100,-10]:storage.fctr64
## 0.4767454204
## spdiff.cut.fctr(-10,-1]:storage.fctr64
## 0.3919319465
## spdiff.cut.fctr(-1,0]:storage.fctr64
## 0.2200754994
## spdiff.cut.fctr(-10,-1]:storage.fctrUnknown
## 1.0012951362
## spdiff.cut.fctr(100,1e+03]:storage.fctrUnknown
## -0.0198880551
## spdiff.cut.fctr(-10,-1]:cellular.fctr0:carrier.fctrNone
## 0.3168461524
## spdiff.cut.fctr(-1,0]:cellular.fctr0:carrier.fctrNone
## 0.3001383223
## spdiff.cut.fctr(10,100]:cellular.fctr0:carrier.fctrNone
## 0.0116905861
## spdiff.cut.fctr(-100,-10]:cellular.fctr1:carrier.fctrSprint
## 0.8145036623
## spdiff.cut.fctr(-100,-10]:cellular.fctr1:carrier.fctrT-Mobile
## 0.1277851826
## spdiff.cut.fctr(-10,-1]:cellular.fctr1:carrier.fctrT-Mobile
## 1.1703566485
## spdiff.cut.fctr(10,100]:cellular.fctr1:carrier.fctrT-Mobile
## 0.6696566185
## spdiff.cut.fctr(100,1e+03]:cellular.fctr1:carrier.fctrUnknown
## 0.1420052475
## spdiff.cut.fctr(100,1e+03]:cellular.fctrUnknown:carrier.fctrUnknown
## -0.0666031786
## spdiff.cut.fctr(-100,-10]:cellular.fctr1:carrier.fctrVerizon
## 0.0954106640
## spdiff.cut.fctr(-10,-1]:cellular.fctr1:carrier.fctrVerizon
## -0.0320095743
## spdiff.cut.fctr(-100,-10]:prdl.my.fctrUnknown:.clusterid.fctr2
## 0.0664162903
## spdiff.cut.fctr(100,1e+03]:prdl.my.fctrUnknown:.clusterid.fctr2
## -0.3109720438
## spdiff.cut.fctr(-100,-10]:prdl.my.fctriPad1:.clusterid.fctr2
## -0.3147875765
## spdiff.cut.fctr(1,10]:prdl.my.fctriPad1:.clusterid.fctr2
## -0.3378382402
## spdiff.cut.fctr(-100,-10]:prdl.my.fctriPad3:.clusterid.fctr2
## -0.1841459126
## spdiff.cut.fctr(-10,-1]:prdl.my.fctriPad3:.clusterid.fctr2
## -0.0264326608
## spdiff.cut.fctr(-100,-10]:prdl.my.fctriPad4:.clusterid.fctr2
## 0.2772797403
## spdiff.cut.fctr(-10,-1]:prdl.my.fctriPadmini2:.clusterid.fctr2
## -0.2232576904
## spdiff.cut.fctr(1,10]:prdl.my.fctriPadmini2:.clusterid.fctr2
## -0.5137811420
## spdiff.cut.fctr(10,100]:prdl.my.fctriPadmini2:.clusterid.fctr2
## 0.4616531730
## spdiff.cut.fctr(-100,-10]:prdl.my.fctrUnknown:.clusterid.fctr3
## -0.8178730193
## spdiff.cut.fctr(1,10]:prdl.my.fctrUnknown:.clusterid.fctr3
## 2.2048462154
## spdiff.cut.fctr(10,100]:prdl.my.fctrUnknown:.clusterid.fctr3
## -0.6462024046
## spdiff.cut.fctr(100,1e+03]:prdl.my.fctrUnknown:.clusterid.fctr3
## -0.2795605686
## spdiff.cut.fctr(-10,-1]:prdl.my.fctriPad1:.clusterid.fctr3
## -0.4939013438
## spdiff.cut.fctr(1,10]:prdl.my.fctriPad1:.clusterid.fctr3
## -0.5366276974
## spdiff.cut.fctr(-10,-1]:prdl.my.fctriPad4:.clusterid.fctr3
## -0.3345996306
## sold.fctr sold.fctr.predict.CSM.X.Interact.Train.glmnet.N
## 1 N 848
## 2 Y 132
## sold.fctr.predict.CSM.X.Interact.Train.glmnet.Y
## 1 147
## 2 723
## Prediction
## Reference N Y
## N 848 147
## Y 132 723
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.491892e-01 6.970151e-01 8.320595e-01 8.652033e-01 5.378378e-01
## AccuracyPValue McnemarPValue
## 3.945222e-178 4.019419e-01
## id
## 1 CSM.X.Interact.Train.glmnet
## feats
## 1 .rnorm,D.weight.sum,D.weight.post.stem.sum,D.chrs.pnct13.n.log,D.ratio.weight.sum.wrds.n,D.ratio.wrds.stop.n.wrds.n,startprice.dcm2.is9,sprice.root2,sprice.log10,startprice.dcm1.is9,D.weight.sum.stem.stop.Ratio,spdiff.cut.fctr*biddable,spdiff.cut.fctr*storage.fctr,spdiff.cut.fctr*prdl.my.fctr,spdiff.cut.fctr*cellular.fctr,spdiff.cut.fctr*D.chrs.pnct11.n.log,spdiff.cut.fctr*condition.fctr,spdiff.cut.fctr*color.fctr,spdiff.cut.fctr*sprice.d20nexp,spdiff.cut.fctr*startprice.dgt2.is9,spdiff.cut.fctr*D.wrds.n.log,spdiff.cut.fctr*D.chrs.n.log,spdiff.cut.fctr*D.chrs.uppr.n.log,spdiff.cut.fctr*D.terms.post.stem.n.log,spdiff.cut.fctr*D.terms.post.stop.n.log,spdiff.cut.fctr*D.wrds.stop.n.log,spdiff.cut.fctr*D.wrds.unq.n.log,spdiff.cut.fctr*startprice.dgt1.is9,spdiff.cut.fctr*D.weight.post.stop.sum,spdiff.cut.fctr*cellular.fctr:carrier.fctr,spdiff.cut.fctr*prdl.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 112.058 7.555
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8461665 0.9075377 0.7847953 0.9193999
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.8382609 0.8212657
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8320595 0.8652033 0.637547
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.009559621 0.02005754## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.4## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.4
## [1] "fitting model: Max.cor.Y.rcv.1X1.cp.0.Train.rpart"
## [1] " indep_vars: biddable,sprice.root2"
## + Fold1.Rep1: cp=0.001754
## - Fold1.Rep1: cp=0.001754
## + Fold2.Rep1: cp=0.001754
## - Fold2.Rep1: cp=0.001754
## + Fold3.Rep1: cp=0.001754
## - Fold3.Rep1: cp=0.001754
## + Fold1.Rep2: cp=0.001754
## - Fold1.Rep2: cp=0.001754
## + Fold2.Rep2: cp=0.001754
## - Fold2.Rep2: cp=0.001754
## + Fold3.Rep2: cp=0.001754
## - Fold3.Rep2: cp=0.001754
## + Fold1.Rep3: cp=0.001754
## - Fold1.Rep3: cp=0.001754
## + Fold2.Rep3: cp=0.001754
## - Fold2.Rep3: cp=0.001754
## + Fold3.Rep3: cp=0.001754
## - Fold3.Rep3: cp=0.001754
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.00351 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 1850
##
## CP nsplit rel error
## 1 0.515789474 0 1.0000000
## 2 0.011695906 1 0.4842105
## 3 0.004678363 3 0.4608187
## 4 0.003508772 7 0.4421053
##
## Variable importance
## biddable sprice.root2
## 58 42
##
## Node number 1: 1850 observations, complexity param=0.5157895
## predicted class=N expected loss=0.4621622 P(node) =1
## class counts: 995 855
## probabilities: 0.538 0.462
## left son=2 (1015 obs) right son=3 (835 obs)
## Primary splits:
## biddable < 0.5 to the left, improve=277.4446, (0 missing)
## sprice.root2 < 10.02494 to the right, improve=217.5035, (0 missing)
## Surrogate splits:
## sprice.root2 < 10.02494 to the right, agree=0.75, adj=0.447, (0 split)
##
## Node number 2: 1015 observations
## predicted class=N expected loss=0.2137931 P(node) =0.5486486
## class counts: 798 217
## probabilities: 0.786 0.214
##
## Node number 3: 835 observations, complexity param=0.01169591
## predicted class=Y expected loss=0.2359281 P(node) =0.4513514
## class counts: 197 638
## probabilities: 0.236 0.764
## left son=6 (293 obs) right son=7 (542 obs)
## Primary splits:
## sprice.root2 < 11.83195 to the right, improve=63.77111, (0 missing)
##
## Node number 6: 293 observations, complexity param=0.01169591
## predicted class=N expected loss=0.4982935 P(node) =0.1583784
## class counts: 147 146
## probabilities: 0.502 0.498
## left son=12 (38 obs) right son=13 (255 obs)
## Primary splits:
## sprice.root2 < 20.33187 to the right, improve=5.969295, (0 missing)
##
## Node number 7: 542 observations
## predicted class=Y expected loss=0.09225092 P(node) =0.292973
## class counts: 50 492
## probabilities: 0.092 0.908
##
## Node number 12: 38 observations
## predicted class=N expected loss=0.2368421 P(node) =0.02054054
## class counts: 29 9
## probabilities: 0.763 0.237
##
## Node number 13: 255 observations, complexity param=0.004678363
## predicted class=Y expected loss=0.4627451 P(node) =0.1378378
## class counts: 118 137
## probabilities: 0.463 0.537
## left son=26 (14 obs) right son=27 (241 obs)
## Primary splits:
## sprice.root2 < 12.24684 to the left, improve=0.9610958, (0 missing)
##
## Node number 26: 14 observations
## predicted class=N expected loss=0.3571429 P(node) =0.007567568
## class counts: 9 5
## probabilities: 0.643 0.357
##
## Node number 27: 241 observations, complexity param=0.004678363
## predicted class=Y expected loss=0.4522822 P(node) =0.1302703
## class counts: 109 132
## probabilities: 0.452 0.548
## left son=54 (233 obs) right son=55 (8 obs)
## Primary splits:
## sprice.root2 < 12.24724 to the right, improve=3.385322, (0 missing)
##
## Node number 54: 233 observations, complexity param=0.004678363
## predicted class=Y expected loss=0.4678112 P(node) =0.1259459
## class counts: 109 124
## probabilities: 0.468 0.532
## left son=108 (129 obs) right son=109 (104 obs)
## Primary splits:
## sprice.root2 < 14.35185 to the right, improve=0.7518126, (0 missing)
##
## Node number 55: 8 observations
## predicted class=Y expected loss=0 P(node) =0.004324324
## class counts: 0 8
## probabilities: 0.000 1.000
##
## Node number 108: 129 observations, complexity param=0.004678363
## predicted class=N expected loss=0.496124 P(node) =0.06972973
## class counts: 65 64
## probabilities: 0.504 0.496
## left son=216 (22 obs) right son=217 (107 obs)
## Primary splits:
## sprice.root2 < 15.52398 to the left, improve=3.834272, (0 missing)
##
## Node number 109: 104 observations
## predicted class=Y expected loss=0.4230769 P(node) =0.05621622
## class counts: 44 60
## probabilities: 0.423 0.577
##
## Node number 216: 22 observations
## predicted class=N expected loss=0.2272727 P(node) =0.01189189
## class counts: 17 5
## probabilities: 0.773 0.227
##
## Node number 217: 107 observations
## predicted class=Y expected loss=0.4485981 P(node) =0.05783784
## class counts: 48 59
## probabilities: 0.449 0.551
##
## n= 1850
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 1850 855 N (0.53783784 0.46216216)
## 2) biddable< 0.5 1015 217 N (0.78620690 0.21379310) *
## 3) biddable>=0.5 835 197 Y (0.23592814 0.76407186)
## 6) sprice.root2>=11.83195 293 146 N (0.50170648 0.49829352)
## 12) sprice.root2>=20.33187 38 9 N (0.76315789 0.23684211) *
## 13) sprice.root2< 20.33187 255 118 Y (0.46274510 0.53725490)
## 26) sprice.root2< 12.24684 14 5 N (0.64285714 0.35714286) *
## 27) sprice.root2>=12.24684 241 109 Y (0.45228216 0.54771784)
## 54) sprice.root2>=12.24724 233 109 Y (0.46781116 0.53218884)
## 108) sprice.root2>=14.35185 129 64 N (0.50387597 0.49612403)
## 216) sprice.root2< 15.52398 22 5 N (0.77272727 0.22727273) *
## 217) sprice.root2>=15.52398 107 48 Y (0.44859813 0.55140187) *
## 109) sprice.root2< 14.35185 104 44 Y (0.42307692 0.57692308) *
## 55) sprice.root2< 12.24724 8 0 Y (0.00000000 1.00000000) *
## 7) sprice.root2< 11.83195 542 50 Y (0.09225092 0.90774908) *
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.Train.rpart.N
## 1 N 853
## 2 Y 236
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.Train.rpart.Y
## 1 142
## 2 619
## Prediction
## Reference N Y
## N 853 142
## Y 236 619
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.956757e-01 5.857939e-01 7.765698e-01 8.138392e-01 5.378378e-01
## AccuracyPValue McnemarPValue
## 1.386956e-118 1.723514e-06
## id feats max.nTuningRuns
## 1 Max.cor.Y.rcv.1X1.cp.0.Train.rpart biddable,sprice.root2 5
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 1.937 0.016 0.7906315
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.8572864 0.7239766 0.8165347 0.5
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.7660891 0.7783853 0.7765698
## max.AccuracyUpper.fit max.Kappa.fit max.AccuracySD.fit max.KappaSD.fit
## 1 0.8138392 0.5492395 0.01626935 0.0367951## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.3## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.3## [1] "fitting model: CSM.X.center.scale.Train.bagEarth"
## [1] " indep_vars: spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr"
## + : degree=1, nprune=64## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## - : degree=1, nprune=64
## Aggregating results
## Fitting final model on full training set## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
##
## Call:
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"1849", "1850")), degree = 1, nprune = 64, keepxy = TRUE, glm = structure(list( family = function (link = "logit") { linktemp <- substitute(link) if (!is.character(linktemp)) linktemp <- deparse(linktemp) okLinks <- c("logit", "probit", "cloglog", "cauchit", "log") if (linktemp %in% okLinks) stats <- make.link(linktemp) else if (is.character(link)) { stats <- make.link(link) linktemp <- link } else { if (inherits(link, "link-glm")) { stats <- link if (!is.null(stats$name)) linktemp <- stats$name } else { stop(gettextf("link \"%s\" not available for binomial family; available links are %s", linktemp, paste(sQuote(okLinks), collapse = ", ")), domain = NA) } } variance <- function(mu) mu * (1 - mu) validmu <- function(mu) all(is.finite(mu)) && all(mu > 0 & mu < 1) dev.resids <- function(y, mu, wt) .Call(C_binomial_dev_resids, y, mu, wt) aic <- function(y, n, mu, wt, dev) { m <- if (any(n > 1)) n else wt -2 * sum(ifelse(m > 0, (wt/m), 0) * dbinom(round(m * y), round(m), mu, log = TRUE)) } initialize <- expression({ if (NCOL(y) == 1) { if (is.factor(y)) y <- y != levels(y)[1L] n <- rep.int(1, nobs) y[weights == 0] <- 0 if (any(y < 0 | y > 1)) stop("y values must be 0 <= y <= 1") mustart <- (weights * y + 0.5)/(weights + 1) m <- weights * y if (any(abs(m - round(m)) > 0.001)) warning("non-integer #successes in a binomial glm!") } else if (NCOL(y) == 2) { if (any(abs(y - round(y)) > 0.001)) warning("non-integer counts in a binomial glm!") n <- y[, 1] + y[, 2] y <- ifelse(n == 0, 0, y[, 1]/n) weights <- weights * n mustart <- (n * y + 0.5)/(n + 1) } else stop("for the 'binomial' family, y must be a vector of 0 and 1's\nor a 2 column matrix where col 1 is no. successes and col 2 is no. failures") }) simfun <- function(object, nsim) { ftd <- fitted(object) n <- length(ftd) ntot <- n * nsim wts <- object$prior.weights if (any(wts%%1 != 0)) stop("cannot simulate from non-integer prior.weights") if (!is.null(m <- object$model)) { y <- model.response(m) if (is.factor(y)) { yy <- factor(1 + rbinom(ntot, size = 1, prob = ftd), labels = levels(y)) split(yy, rep(seq_len(nsim), each = n)) } else if (is.matrix(y) && ncol(y) == 2) { yy <- vector("list", nsim) for (i in seq_len(nsim)) { Y <- rbinom(n, size = wts, prob = ftd) YY <- cbind(Y, wts - Y) colnames(YY) <- colnames(y) yy[[i]] <- YY } yy } else rbinom(ntot, size = wts, prob = ftd)/wts } else rbinom(ntot, size = wts, prob = ftd)/wts } structure(list(family = "binomial", link = linktemp, linkfun = stats$linkfun, linkinv = stats$linkinv, variance = variance, dev.resids = dev.resids, aic = aic, mu.eta = stats$mu.eta, initialize = initialize, validmu = validmu, valideta = stats$valideta, simulate = simfun), class = "family") }), .Names = "family"))
##
## Out of bag statistics:
##
## Accuracy Kappa
## 0% 0.7771 0.5520
## 2.5% 0.7850 0.5650
## 25% 0.8007 0.5991
## 50% 0.8082 0.6144
## 75% 0.8184 0.6320
## 97.5% 0.8322 0.6586
## 100% 0.8447 0.6829
##
## Model Selection Statistics:
##
## Num Terms Num Variables
## Length:1563 Length:1563
## Class :character Class :character
## Mode :character Mode :character
## sold.fctr sold.fctr.predict.CSM.X.center.scale.Train.bagEarth.N
## 1 N 778
## 2 Y 75
## sold.fctr.predict.CSM.X.center.scale.Train.bagEarth.Y
## 1 217
## 2 780
## Prediction
## Reference N Y
## N 778 217
## Y 75 780
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.421622e-01 6.861729e-01 8.247380e-01 8.584936e-01 5.378378e-01
## AccuracyPValue McnemarPValue
## 2.200963e-169 1.565485e-16
## id
## 1 CSM.X.center.scale.Train.bagEarth
## feats
## 1 spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 157.328 78.343
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8547651 0.9025126 0.8070175 0.9340316
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.3 0.8423326 0.8108098
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.824738 0.8584936 0.6163134## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.5## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.5## [1] "fitting model: CSM.X.Train.bagEarth"
## [1] " indep_vars: spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr"
## + : degree=1, nprune=64## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## - : degree=1, nprune=64
## Aggregating results
## Fitting final model on full training set## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
##
## Call:
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"1849", "1850")), degree = 1, nprune = 64, keepxy = TRUE, glm = structure(list( family = function (link = "logit") { linktemp <- substitute(link) if (!is.character(linktemp)) linktemp <- deparse(linktemp) okLinks <- c("logit", "probit", "cloglog", "cauchit", "log") if (linktemp %in% okLinks) stats <- make.link(linktemp) else if (is.character(link)) { stats <- make.link(link) linktemp <- link } else { if (inherits(link, "link-glm")) { stats <- link if (!is.null(stats$name)) linktemp <- stats$name } else { stop(gettextf("link \"%s\" not available for binomial family; available links are %s", linktemp, paste(sQuote(okLinks), collapse = ", ")), domain = NA) } } variance <- function(mu) mu * (1 - mu) validmu <- function(mu) all(is.finite(mu)) && all(mu > 0 & mu < 1) dev.resids <- function(y, mu, wt) .Call(C_binomial_dev_resids, y, mu, wt) aic <- function(y, n, mu, wt, dev) { m <- if (any(n > 1)) n else wt -2 * sum(ifelse(m > 0, (wt/m), 0) * dbinom(round(m * y), round(m), mu, log = TRUE)) } initialize <- expression({ if (NCOL(y) == 1) { if (is.factor(y)) y <- y != levels(y)[1L] n <- rep.int(1, nobs) y[weights == 0] <- 0 if (any(y < 0 | y > 1)) stop("y values must be 0 <= y <= 1") mustart <- (weights * y + 0.5)/(weights + 1) m <- weights * y if (any(abs(m - round(m)) > 0.001)) warning("non-integer #successes in a binomial glm!") } else if (NCOL(y) == 2) { if (any(abs(y - round(y)) > 0.001)) warning("non-integer counts in a binomial glm!") n <- y[, 1] + y[, 2] y <- ifelse(n == 0, 0, y[, 1]/n) weights <- weights * n mustart <- (n * y + 0.5)/(n + 1) } else stop("for the 'binomial' family, y must be a vector of 0 and 1's\nor a 2 column matrix where col 1 is no. successes and col 2 is no. failures") }) simfun <- function(object, nsim) { ftd <- fitted(object) n <- length(ftd) ntot <- n * nsim wts <- object$prior.weights if (any(wts%%1 != 0)) stop("cannot simulate from non-integer prior.weights") if (!is.null(m <- object$model)) { y <- model.response(m) if (is.factor(y)) { yy <- factor(1 + rbinom(ntot, size = 1, prob = ftd), labels = levels(y)) split(yy, rep(seq_len(nsim), each = n)) } else if (is.matrix(y) && ncol(y) == 2) { yy <- vector("list", nsim) for (i in seq_len(nsim)) { Y <- rbinom(n, size = wts, prob = ftd) YY <- cbind(Y, wts - Y) colnames(YY) <- colnames(y) yy[[i]] <- YY } yy } else rbinom(ntot, size = wts, prob = ftd)/wts } else rbinom(ntot, size = wts, prob = ftd)/wts } structure(list(family = "binomial", link = linktemp, linkfun = stats$linkfun, linkinv = stats$linkinv, variance = variance, dev.resids = dev.resids, aic = aic, mu.eta = stats$mu.eta, initialize = initialize, validmu = validmu, valideta = stats$valideta, simulate = simfun), class = "family") }), .Names = "family"))
##
## Out of bag statistics:
##
## Accuracy Kappa
## 0% 0.7771 0.5520
## 2.5% 0.7850 0.5650
## 25% 0.8007 0.5991
## 50% 0.8082 0.6144
## 75% 0.8184 0.6320
## 97.5% 0.8322 0.6586
## 100% 0.8447 0.6829
##
## Model Selection Statistics:
##
## Num Terms Num Variables
## Length:1563 Length:1563
## Class :character Class :character
## Mode :character Mode :character
## sold.fctr sold.fctr.predict.CSM.X.Train.bagEarth.N
## 1 N 778
## 2 Y 75
## sold.fctr.predict.CSM.X.Train.bagEarth.Y
## 1 217
## 2 780
## Prediction
## Reference N Y
## N 778 217
## Y 75 780
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.421622e-01 6.861729e-01 8.247380e-01 8.584936e-01 5.378378e-01
## AccuracyPValue McnemarPValue
## 2.200963e-169 1.565485e-16
## id
## 1 CSM.X.Train.bagEarth
## feats
## 1 spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 154.684 77.575
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8547651 0.9025126 0.8070175 0.9340316
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.3 0.8423326 0.8108098
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.824738 0.8584936 0.6163134## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.5## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.5## [1] "fitting model: CSM.X.YeoJohnson.Train.bagEarth"
## [1] " indep_vars: spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr"
## + : degree=1, nprune=64## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## - : degree=1, nprune=64
## Aggregating results
## Fitting final model on full training set## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
##
## Call:
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"1849", "1850")), degree = 1, nprune = 64, keepxy = TRUE, glm = structure(list( family = function (link = "logit") { linktemp <- substitute(link) if (!is.character(linktemp)) linktemp <- deparse(linktemp) okLinks <- c("logit", "probit", "cloglog", "cauchit", "log") if (linktemp %in% okLinks) stats <- make.link(linktemp) else if (is.character(link)) { stats <- make.link(link) linktemp <- link } else { if (inherits(link, "link-glm")) { stats <- link if (!is.null(stats$name)) linktemp <- stats$name } else { stop(gettextf("link \"%s\" not available for binomial family; available links are %s", linktemp, paste(sQuote(okLinks), collapse = ", ")), domain = NA) } } variance <- function(mu) mu * (1 - mu) validmu <- function(mu) all(is.finite(mu)) && all(mu > 0 & mu < 1) dev.resids <- function(y, mu, wt) .Call(C_binomial_dev_resids, y, mu, wt) aic <- function(y, n, mu, wt, dev) { m <- if (any(n > 1)) n else wt -2 * sum(ifelse(m > 0, (wt/m), 0) * dbinom(round(m * y), round(m), mu, log = TRUE)) } initialize <- expression({ if (NCOL(y) == 1) { if (is.factor(y)) y <- y != levels(y)[1L] n <- rep.int(1, nobs) y[weights == 0] <- 0 if (any(y < 0 | y > 1)) stop("y values must be 0 <= y <= 1") mustart <- (weights * y + 0.5)/(weights + 1) m <- weights * y if (any(abs(m - round(m)) > 0.001)) warning("non-integer #successes in a binomial glm!") } else if (NCOL(y) == 2) { if (any(abs(y - round(y)) > 0.001)) warning("non-integer counts in a binomial glm!") n <- y[, 1] + y[, 2] y <- ifelse(n == 0, 0, y[, 1]/n) weights <- weights * n mustart <- (n * y + 0.5)/(n + 1) } else stop("for the 'binomial' family, y must be a vector of 0 and 1's\nor a 2 column matrix where col 1 is no. successes and col 2 is no. failures") }) simfun <- function(object, nsim) { ftd <- fitted(object) n <- length(ftd) ntot <- n * nsim wts <- object$prior.weights if (any(wts%%1 != 0)) stop("cannot simulate from non-integer prior.weights") if (!is.null(m <- object$model)) { y <- model.response(m) if (is.factor(y)) { yy <- factor(1 + rbinom(ntot, size = 1, prob = ftd), labels = levels(y)) split(yy, rep(seq_len(nsim), each = n)) } else if (is.matrix(y) && ncol(y) == 2) { yy <- vector("list", nsim) for (i in seq_len(nsim)) { Y <- rbinom(n, size = wts, prob = ftd) YY <- cbind(Y, wts - Y) colnames(YY) <- colnames(y) yy[[i]] <- YY } yy } else rbinom(ntot, size = wts, prob = ftd)/wts } else rbinom(ntot, size = wts, prob = ftd)/wts } structure(list(family = "binomial", link = linktemp, linkfun = stats$linkfun, linkinv = stats$linkinv, variance = variance, dev.resids = dev.resids, aic = aic, mu.eta = stats$mu.eta, initialize = initialize, validmu = validmu, valideta = stats$valideta, simulate = simfun), class = "family") }), .Names = "family"))
##
## Out of bag statistics:
##
## Accuracy Kappa
## 0% 0.7771 0.5520
## 2.5% 0.7850 0.5650
## 25% 0.8007 0.5991
## 50% 0.8082 0.6144
## 75% 0.8184 0.6320
## 97.5% 0.8322 0.6586
## 100% 0.8447 0.6829
##
## Model Selection Statistics:
##
## Num Terms Num Variables
## Length:1563 Length:1563
## Class :character Class :character
## Mode :character Mode :character
## sold.fctr sold.fctr.predict.CSM.X.YeoJohnson.Train.bagEarth.N
## 1 N 778
## 2 Y 75
## sold.fctr.predict.CSM.X.YeoJohnson.Train.bagEarth.Y
## 1 217
## 2 780
## Prediction
## Reference N Y
## N 778 217
## Y 75 780
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.421622e-01 6.861729e-01 8.247380e-01 8.584936e-01 5.378378e-01
## AccuracyPValue McnemarPValue
## 2.200963e-169 1.565485e-16
## id
## 1 CSM.X.YeoJohnson.Train.bagEarth
## feats
## 1 spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 156.526 78.321
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8547651 0.9025126 0.8070175 0.9340316
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.3 0.8423326 0.8108098
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.824738 0.8584936 0.6163134## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.4## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.4
## [1] "fitting model: CSM.X.Train.nnet"
## [1] " indep_vars: spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting size = 1, decay = 0.1 on full training set
## # weights: 103
## initial value 1316.650695
## iter 10 value 951.787762
## iter 20 value 813.977272
## iter 30 value 763.292569
## iter 40 value 721.783636
## iter 50 value 692.487191
## iter 60 value 678.654915
## iter 70 value 673.476215
## iter 80 value 672.174165
## iter 90 value 671.859048
## iter 100 value 671.847685
## final value 671.847685
## stopped after 100 iterations## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdlIdPfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: size## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdlIdPfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: decay
## a 100-1-1 network with 103 weights
## options were - entropy fitting decay=0.1
## b->h1 i1->h1 i2->h1 i3->h1 i4->h1 i5->h1 i6->h1 i7->h1
## -1.42 0.01 0.53 0.08 0.18 -0.08 0.02 1.85
## i8->h1 i9->h1 i10->h1 i11->h1 i12->h1 i13->h1 i14->h1 i15->h1
## -0.55 2.79 0.30 -0.54 0.30 -1.28 -1.89 -0.04
## i16->h1 i17->h1 i18->h1 i19->h1 i20->h1 i21->h1 i22->h1 i23->h1
## -0.55 -0.67 -0.16 0.03 0.01 0.04 0.10 0.50
## i24->h1 i25->h1 i26->h1 i27->h1 i28->h1 i29->h1 i30->h1 i31->h1
## 0.14 -0.12 -0.38 0.42 0.65 0.25 -0.13 -0.53
## i32->h1 i33->h1 i34->h1 i35->h1 i36->h1 i37->h1 i38->h1 i39->h1
## -0.74 -0.97 0.09 -0.41 -0.35 -1.40 -2.37 -2.77
## i40->h1 i41->h1 i42->h1 i43->h1 i44->h1 i45->h1 i46->h1 i47->h1
## -2.54 -2.13 -1.77 0.46 -1.35 -0.13 0.38 0.07
## i48->h1 i49->h1 i50->h1 i51->h1 i52->h1 i53->h1 i54->h1 i55->h1
## 0.34 -0.12 -0.05 -0.06 0.55 0.54 0.21 0.23
## i56->h1 i57->h1 i58->h1 i59->h1 i60->h1 i61->h1 i62->h1 i63->h1
## -0.58 0.00 0.00 0.00 -0.35 0.00 0.00 -0.26
## i64->h1 i65->h1 i66->h1 i67->h1 i68->h1 i69->h1 i70->h1 i71->h1
## 0.00 0.00 -0.18 0.00 0.00 0.07 -0.16 0.00
## i72->h1 i73->h1 i74->h1 i75->h1 i76->h1 i77->h1 i78->h1 i79->h1
## -0.07 0.00 -0.71 0.33 0.00 0.41 0.18 -0.24
## i80->h1 i81->h1 i82->h1 i83->h1 i84->h1 i85->h1 i86->h1 i87->h1
## 0.00 0.00 0.28 0.00 -0.18 0.76 0.00 0.00
## i88->h1 i89->h1 i90->h1 i91->h1 i92->h1 i93->h1 i94->h1 i95->h1
## 2.38 0.00 0.00 0.00 0.00 0.00 0.43 0.85
## i96->h1 i97->h1 i98->h1 i99->h1 i100->h1
## -0.69 -0.43 0.35 0.11 -2.32
## b->o h1->o
## 4.08 -8.52
## sold.fctr sold.fctr.predict.CSM.X.Train.nnet.N
## 1 N 893
## 2 Y 175
## sold.fctr.predict.CSM.X.Train.nnet.Y
## 1 102
## 2 680
## Prediction
## Reference N Y
## N 893 102
## Y 175 680
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.502703e-01 6.969957e-01 8.331870e-01 8.662345e-01 5.378378e-01
## AccuracyPValue McnemarPValue
## 1.672257e-179 1.517996e-05
## id
## 1 CSM.X.Train.nnet
## feats
## 1 spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 65.06 0.389
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8464045 0.8974874 0.7953216 0.9200882
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.830788 0.8216195
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.833187 0.8662345 0.6391955
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01079087 0.02185845## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.5## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.5## [1] "fitting model: CSM.X.Train.glm"
## [1] " indep_vars: spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr"
## + Fold1.Rep1: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold1.Rep1: parameter=none
## + Fold2.Rep1: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold2.Rep1: parameter=none
## + Fold3.Rep1: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold3.Rep1: parameter=none
## + Fold1.Rep2: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold1.Rep2: parameter=none
## + Fold2.Rep2: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold2.Rep2: parameter=none
## + Fold3.Rep2: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold3.Rep2: parameter=none
## + Fold1.Rep3: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold1.Rep3: parameter=none
## + Fold2.Rep3: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold2.Rep3: parameter=none
## + Fold3.Rep3: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold3.Rep3: parameter=none
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.0041 -0.5683 -0.1125 0.4343 3.4232
##
## Coefficients: (26 not defined because of singularities)
## Estimate Std. Error z value
## (Intercept) 4.73446 19.94628 0.237
## .rnorm -0.02479 0.07107 -0.349
## D.chrs.n.log -1.95881 1.69942 -1.153
## D.chrs.pnct11.n.log -0.13668 0.27589 -0.495
## D.chrs.pnct13.n.log -0.23784 0.25354 -0.938
## D.chrs.uppr.n.log 0.31806 1.52870 0.208
## D.ratio.weight.sum.wrds.n 0.01013 0.23551 0.043
## D.ratio.wrds.stop.n.wrds.n -6.28637 2.61913 -2.400
## D.terms.post.stem.n.log 11.82358 6.28790 1.880
## D.terms.post.stop.n.log -16.65202 6.33949 -2.627
## D.weight.post.stem.sum -1.46055 3.57191 -0.409
## D.weight.post.stop.sum 1.31051 3.46529 0.378
## D.weight.sum NA NA NA
## D.weight.sum.stem.stop.Ratio 2.46354 20.06686 0.123
## D.wrds.n.log 5.52223 1.63118 3.385
## D.wrds.stop.n.log 0.24160 0.45778 0.528
## D.wrds.unq.n.log NA NA NA
## cellular.fctr1 0.17567 0.23975 0.733
## cellular.fctrUnknown -0.55239 0.36645 -1.507
## color.fctrGold -0.11931 0.48147 -0.248
## `color.fctrSpace Gray` -0.01237 0.28886 -0.043
## color.fctrUnknown -0.08224 0.19840 -0.415
## color.fctrWhite -0.16211 0.21883 -0.741
## `condition.fctrFor parts or not working` -0.75930 0.30027 -2.529
## `condition.fctrManufacturer refurbished` -0.28718 0.50503 -0.569
## condition.fctrNew 0.18744 0.27491 0.682
## `condition.fctrNew other (see details)` 0.67836 0.39747 1.707
## `condition.fctrSeller refurbished` -0.81136 0.33267 -2.439
## prdl.my.fctriPad1 -1.05001 0.44723 -2.348
## prdl.my.fctriPad2 -0.58378 0.39368 -1.483
## prdl.my.fctriPad3 0.05562 0.43455 0.128
## prdl.my.fctriPad4 0.74044 0.45704 1.620
## prdl.my.fctriPadAir 1.15082 0.45175 2.547
## prdl.my.fctriPadAir2 1.44770 0.51041 2.836
## prdl.my.fctriPadmini -0.43296 0.38825 -1.115
## prdl.my.fctriPadmini2 0.42592 0.46925 0.908
## prdl.my.fctriPadmini3 0.38069 0.53962 0.705
## `spdiff.cut.fctr(-100,-10]` 2.60495 0.63528 4.100
## `spdiff.cut.fctr(-10,-1]` 4.55325 0.68089 6.687
## `spdiff.cut.fctr(-1,0]` 5.44829 0.94208 5.783
## `spdiff.cut.fctr(0,1]` 5.03206 1.19232 4.220
## `spdiff.cut.fctr(1,10]` 4.10512 0.68397 6.002
## `spdiff.cut.fctr(10,100]` 3.40322 0.66217 5.140
## `spdiff.cut.fctr(100,1e+03]` 0.31805 0.92273 0.345
## biddable 2.51918 0.66730 3.775
## sprice.d20nexp -1.47380 1.87859 -0.785
## sprice.log10 -0.37829 0.51081 -0.741
## sprice.root2 -0.14525 0.08111 -1.791
## startprice.dcm1.is9 -0.68365 0.26780 -2.553
## startprice.dcm2.is9 0.20900 0.26538 0.788
## startprice.dgt1.is9 0.12259 0.18985 0.646
## startprice.dgt2.is9 0.09114 0.21855 0.417
## storage.fctr16 -0.93702 0.45435 -2.062
## storage.fctr32 -0.91016 0.46785 -1.945
## storage.fctr64 -0.33661 0.44700 -0.753
## storage.fctrUnknown -0.25914 0.57132 -0.454
## `cellular.fctr0:carrier.fctrNone` NA NA NA
## `cellular.fctr1:carrier.fctrNone` NA NA NA
## `cellular.fctrUnknown:carrier.fctrNone` NA NA NA
## `cellular.fctr0:carrier.fctrOther` NA NA NA
## `cellular.fctr1:carrier.fctrOther` 15.51047 4586.62105 0.003
## `cellular.fctrUnknown:carrier.fctrOther` NA NA NA
## `cellular.fctr0:carrier.fctrSprint` NA NA NA
## `cellular.fctr1:carrier.fctrSprint` 0.50491 0.56600 0.892
## `cellular.fctrUnknown:carrier.fctrSprint` NA NA NA
## `cellular.fctr0:carrier.fctrT-Mobile` NA NA NA
## `cellular.fctr1:carrier.fctrT-Mobile` 0.34587 0.68841 0.502
## `cellular.fctrUnknown:carrier.fctrT-Mobile` NA NA NA
## `cellular.fctr0:carrier.fctrUnknown` NA NA NA
## `cellular.fctr1:carrier.fctrUnknown` -0.06523 0.40947 -0.159
## `cellular.fctrUnknown:carrier.fctrUnknown` NA NA NA
## `cellular.fctr0:carrier.fctrVerizon` NA NA NA
## `cellular.fctr1:carrier.fctrVerizon` 0.16740 0.34092 0.491
## `cellular.fctrUnknown:carrier.fctrVerizon` NA NA NA
## `prdl.my.fctrUnknown:.clusterid.fctr2` 0.95667 0.63465 1.507
## `prdl.my.fctriPad1:.clusterid.fctr2` -1.00478 0.59413 -1.691
## `prdl.my.fctriPad2:.clusterid.fctr2` NA NA NA
## `prdl.my.fctriPad3:.clusterid.fctr2` -0.75349 0.73721 -1.022
## `prdl.my.fctriPad4:.clusterid.fctr2` -0.27229 0.79894 -0.341
## `prdl.my.fctriPadAir:.clusterid.fctr2` 0.45056 0.60406 0.746
## `prdl.my.fctriPadAir2:.clusterid.fctr2` NA NA NA
## `prdl.my.fctriPadmini:.clusterid.fctr2` NA NA NA
## `prdl.my.fctriPadmini2:.clusterid.fctr2` -0.53210 0.88452 -0.602
## `prdl.my.fctriPadmini3:.clusterid.fctr2` NA NA NA
## `prdl.my.fctrUnknown:.clusterid.fctr3` 0.33607 0.83343 0.403
## `prdl.my.fctriPad1:.clusterid.fctr3` -1.58817 0.73089 -2.173
## `prdl.my.fctriPad2:.clusterid.fctr3` NA NA NA
## `prdl.my.fctriPad3:.clusterid.fctr3` NA NA NA
## `prdl.my.fctriPad4:.clusterid.fctr3` -18.29309 1080.11108 -0.017
## `prdl.my.fctriPadAir:.clusterid.fctr3` NA NA NA
## `prdl.my.fctriPadAir2:.clusterid.fctr3` NA NA NA
## `prdl.my.fctriPadmini:.clusterid.fctr3` NA NA NA
## `prdl.my.fctriPadmini2:.clusterid.fctr3` NA NA NA
## `prdl.my.fctriPadmini3:.clusterid.fctr3` NA NA NA
## `spdiff.cut.fctr(-100,-10]:biddable` -0.55261 0.69177 -0.799
## `spdiff.cut.fctr(-10,-1]:biddable` -1.32060 0.79593 -1.659
## `spdiff.cut.fctr(-1,0]:biddable` 13.55142 560.18669 0.024
## `spdiff.cut.fctr(0,1]:biddable` -0.41584 1.66258 -0.250
## `spdiff.cut.fctr(1,10]:biddable` -0.43385 0.83226 -0.521
## `spdiff.cut.fctr(10,100]:biddable` -0.29989 0.75568 -0.397
## `spdiff.cut.fctr(100,1e+03]:biddable` 2.68672 1.40448 1.913
## Pr(>|z|)
## (Intercept) 0.812377
## .rnorm 0.727269
## D.chrs.n.log 0.249058
## D.chrs.pnct11.n.log 0.620302
## D.chrs.pnct13.n.log 0.348207
## D.chrs.uppr.n.log 0.835181
## D.ratio.weight.sum.wrds.n 0.965696
## D.ratio.wrds.stop.n.wrds.n 0.016387 *
## D.terms.post.stem.n.log 0.060058 .
## D.terms.post.stop.n.log 0.008621 **
## D.weight.post.stem.sum 0.682615
## D.weight.post.stop.sum 0.705294
## D.weight.sum NA
## D.weight.sum.stem.stop.Ratio 0.902292
## D.wrds.n.log 0.000711 ***
## D.wrds.stop.n.log 0.597660
## D.wrds.unq.n.log NA
## cellular.fctr1 0.463732
## cellular.fctrUnknown 0.131707
## color.fctrGold 0.804281
## `color.fctrSpace Gray` 0.965853
## color.fctrUnknown 0.678493
## color.fctrWhite 0.458821
## `condition.fctrFor parts or not working` 0.011448 *
## `condition.fctrManufacturer refurbished` 0.569597
## condition.fctrNew 0.495357
## `condition.fctrNew other (see details)` 0.087878 .
## `condition.fctrSeller refurbished` 0.014730 *
## prdl.my.fctriPad1 0.018884 *
## prdl.my.fctriPad2 0.138109
## prdl.my.fctriPad3 0.898146
## prdl.my.fctriPad4 0.105217
## prdl.my.fctriPadAir 0.010850 *
## prdl.my.fctriPadAir2 0.004563 **
## prdl.my.fctriPadmini 0.264785
## prdl.my.fctriPadmini2 0.364060
## prdl.my.fctriPadmini3 0.480515
## `spdiff.cut.fctr(-100,-10]` 4.12e-05 ***
## `spdiff.cut.fctr(-10,-1]` 2.27e-11 ***
## `spdiff.cut.fctr(-1,0]` 7.33e-09 ***
## `spdiff.cut.fctr(0,1]` 2.44e-05 ***
## `spdiff.cut.fctr(1,10]` 1.95e-09 ***
## `spdiff.cut.fctr(10,100]` 2.75e-07 ***
## `spdiff.cut.fctr(100,1e+03]` 0.730334
## biddable 0.000160 ***
## sprice.d20nexp 0.432733
## sprice.log10 0.458949
## sprice.root2 0.073334 .
## startprice.dcm1.is9 0.010684 *
## startprice.dcm2.is9 0.430967
## startprice.dgt1.is9 0.518446
## startprice.dgt2.is9 0.676667
## storage.fctr16 0.039174 *
## storage.fctr32 0.051726 .
## storage.fctr64 0.451428
## storage.fctrUnknown 0.650130
## `cellular.fctr0:carrier.fctrNone` NA
## `cellular.fctr1:carrier.fctrNone` NA
## `cellular.fctrUnknown:carrier.fctrNone` NA
## `cellular.fctr0:carrier.fctrOther` NA
## `cellular.fctr1:carrier.fctrOther` 0.997302
## `cellular.fctrUnknown:carrier.fctrOther` NA
## `cellular.fctr0:carrier.fctrSprint` NA
## `cellular.fctr1:carrier.fctrSprint` 0.372352
## `cellular.fctrUnknown:carrier.fctrSprint` NA
## `cellular.fctr0:carrier.fctrT-Mobile` NA
## `cellular.fctr1:carrier.fctrT-Mobile` 0.615370
## `cellular.fctrUnknown:carrier.fctrT-Mobile` NA
## `cellular.fctr0:carrier.fctrUnknown` NA
## `cellular.fctr1:carrier.fctrUnknown` 0.873420
## `cellular.fctrUnknown:carrier.fctrUnknown` NA
## `cellular.fctr0:carrier.fctrVerizon` NA
## `cellular.fctr1:carrier.fctrVerizon` 0.623414
## `cellular.fctrUnknown:carrier.fctrVerizon` NA
## `prdl.my.fctrUnknown:.clusterid.fctr2` 0.131706
## `prdl.my.fctriPad1:.clusterid.fctr2` 0.090804 .
## `prdl.my.fctriPad2:.clusterid.fctr2` NA
## `prdl.my.fctriPad3:.clusterid.fctr2` 0.306739
## `prdl.my.fctriPad4:.clusterid.fctr2` 0.733243
## `prdl.my.fctriPadAir:.clusterid.fctr2` 0.455735
## `prdl.my.fctriPadAir2:.clusterid.fctr2` NA
## `prdl.my.fctriPadmini:.clusterid.fctr2` NA
## `prdl.my.fctriPadmini2:.clusterid.fctr2` 0.547466
## `prdl.my.fctriPadmini3:.clusterid.fctr2` NA
## `prdl.my.fctrUnknown:.clusterid.fctr3` 0.686771
## `prdl.my.fctriPad1:.clusterid.fctr3` 0.029785 *
## `prdl.my.fctriPad2:.clusterid.fctr3` NA
## `prdl.my.fctriPad3:.clusterid.fctr3` NA
## `prdl.my.fctriPad4:.clusterid.fctr3` 0.986487
## `prdl.my.fctriPadAir:.clusterid.fctr3` NA
## `prdl.my.fctriPadAir2:.clusterid.fctr3` NA
## `prdl.my.fctriPadmini:.clusterid.fctr3` NA
## `prdl.my.fctriPadmini2:.clusterid.fctr3` NA
## `prdl.my.fctriPadmini3:.clusterid.fctr3` NA
## `spdiff.cut.fctr(-100,-10]:biddable` 0.424384
## `spdiff.cut.fctr(-10,-1]:biddable` 0.097075 .
## `spdiff.cut.fctr(-1,0]:biddable` 0.980700
## `spdiff.cut.fctr(0,1]:biddable` 0.802497
## `spdiff.cut.fctr(1,10]:biddable` 0.602162
## `spdiff.cut.fctr(10,100]:biddable` 0.691478
## `spdiff.cut.fctr(100,1e+03]:biddable` 0.055752 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2554.0 on 1849 degrees of freedom
## Residual deviance: 1313.3 on 1775 degrees of freedom
## AIC: 1463.3
##
## Number of Fisher Scoring iterations: 17## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## sold.fctr sold.fctr.predict.CSM.X.Train.glm.N
## 1 N 826
## 2 Y 131
## sold.fctr.predict.CSM.X.Train.glm.Y
## 1 169
## 2 724
## Prediction
## Reference N Y
## N 826 169
## Y 131 724
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.378378e-01 6.748244e-01 8.202383e-01 8.543588e-01 5.378378e-01
## AccuracyPValue McnemarPValue
## 3.770097e-164 3.266344e-02
## id
## 1 CSM.X.Train.glm
## feats
## 1 spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 4.842 0.44
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8423933 0.8964824 0.7883041 0.9199177
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.8283753 0.8172966
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8202383 0.8543588 0.6306829
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.009395062 0.01933655## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.5## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.5## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## [1] "fitting model: All.X.Train.glmnet"
## [1] " indep_vars: biddable,sprice.d20nexp,spdiff.cut.fctr,D.ratio.wrds.stop.n.wrds.n,startprice.dcm2.is9,D.wrds.stop.n.log,D.weight.sum.stem.stop.Ratio,D.ratio.weight.sum.wrds.n,.rnorm,startprice.dcm1.is9,storage.fctr,D.chrs.pnct11.n.log,D.chrs.pnct13.n.log,startprice.dgt2.is9,color.fctr,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,prdl.my.fctr,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.n.log,D.terms.post.stem.n.log,D.wrds.unq.n.log,D.terms.post.stop.n.log,cellular.fctr,startprice.dgt1.is9,condition.fctr,sprice.log10,sprice.root2,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.00548 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdlIdPfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha
## Length Class Mode
## a0 85 -none- numeric
## beta 7905 dgCMatrix S4
## df 85 -none- numeric
## dim 2 -none- numeric
## lambda 85 -none- numeric
## dev.ratio 85 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 93 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## -0.5748424332
## D.chrs.pnct13.n.log
## -0.0154166095
## D.ratio.weight.sum.wrds.n
## -0.0004393607
## D.terms.post.stop.n.log
## -0.0089438359
## biddable
## 1.9394377386
## cellular.fctr1
## 0.0702567191
## cellular.fctrUnknown
## -0.1320135490
## color.fctrWhite
## -0.0266151806
## condition.fctrFor parts or not working
## -0.2200229713
## condition.fctrNew other (see details)
## 0.4387934720
## condition.fctrSeller refurbished
## -0.4201823354
## prdl.my.fctriPad1
## -0.4548555560
## prdl.my.fctriPad2
## -0.3245612053
## prdl.my.fctriPad4
## 0.1977145978
## prdl.my.fctriPadAir
## 0.6128927100
## prdl.my.fctriPadAir2
## 0.8968352444
## prdl.my.fctriPadmini
## -0.1622395269
## prdl.my.fctriPadmini2
## 0.1674866134
## spdiff.cut.fctr(-100,-10]
## 1.1267058623
## spdiff.cut.fctr(-10,-1]
## 2.6792229681
## spdiff.cut.fctr(-1,0]
## 3.6061648196
## spdiff.cut.fctr(0,1]
## 2.7445701555
## spdiff.cut.fctr(1,10]
## 2.4590410900
## spdiff.cut.fctr(10,100]
## 2.0111381095
## spdiff.cut.fctr(100,1e+03]
## 0.2412671988
## sprice.log10
## -0.1516075270
## sprice.root2
## -0.0864776262
## startprice.dcm1.is9
## -0.2865501177
## storage.fctr32
## -0.0032900963
## storage.fctr64
## 0.2388740478
## cellular.fctr1:carrier.fctrSprint
## 0.1795038991
## cellular.fctr1:carrier.fctrT-Mobile
## 0.0553779569
## cellular.fctrUnknown:carrier.fctrUnknown
## -0.0076892795
## prdl.my.fctrUnknown:.clusterid.fctr2
## 0.3891983721
## prdl.my.fctriPad1:.clusterid.fctr2
## -0.4118967518
## prdl.my.fctriPad3:.clusterid.fctr2
## -0.2066322688
## prdl.my.fctriPad1:.clusterid.fctr3
## -0.7279651218
## prdl.my.fctriPad4:.clusterid.fctr3
## -2.2439690983
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## -0.4887481635
## D.chrs.pnct11.n.log
## -0.0049897588
## D.chrs.pnct13.n.log
## -0.0343205066
## D.ratio.weight.sum.wrds.n
## -0.0065169343
## D.terms.post.stop.n.log
## -0.0039264020
## D.weight.sum.stem.stop.Ratio
## -0.1489884401
## biddable
## 1.9631938033
## cellular.fctr1
## 0.0786061208
## cellular.fctrUnknown
## -0.1412878868
## color.fctrWhite
## -0.0385898938
## condition.fctrFor parts or not working
## -0.2344565867
## condition.fctrNew other (see details)
## 0.4627975233
## condition.fctrSeller refurbished
## -0.4454800069
## prdl.my.fctriPad1
## -0.4810735152
## prdl.my.fctriPad2
## -0.3463142796
## prdl.my.fctriPad4
## 0.2270111894
## prdl.my.fctriPadAir
## 0.6385121785
## prdl.my.fctriPadAir2
## 0.9150943539
## prdl.my.fctriPadmini
## -0.1801302700
## prdl.my.fctriPadmini2
## 0.1762468108
## spdiff.cut.fctr(-100,-10]
## 1.2080758350
## spdiff.cut.fctr(-10,-1]
## 2.7783245058
## spdiff.cut.fctr(-1,0]
## 3.7489075715
## spdiff.cut.fctr(0,1]
## 2.8862595318
## spdiff.cut.fctr(1,10]
## 2.5615257475
## spdiff.cut.fctr(10,100]
## 2.0983774318
## spdiff.cut.fctr(100,1e+03]
## 0.3228440594
## sprice.log10
## -0.1541621517
## sprice.root2
## -0.0866668571
## startprice.dcm1.is9
## -0.2961675551
## storage.fctr32
## -0.0092455272
## storage.fctr64
## 0.2513803695
## cellular.fctr1:carrier.fctrSprint
## 0.2045698195
## cellular.fctr1:carrier.fctrT-Mobile
## 0.0719134607
## cellular.fctrUnknown:carrier.fctrUnknown
## -0.0119069939
## prdl.my.fctrUnknown:.clusterid.fctr2
## 0.4302836448
## prdl.my.fctriPad1:.clusterid.fctr2
## -0.4315400934
## prdl.my.fctriPad3:.clusterid.fctr2
## -0.2494241814
## prdl.my.fctrUnknown:.clusterid.fctr3
## -0.0005985027
## prdl.my.fctriPad1:.clusterid.fctr3
## -0.7691702044
## prdl.my.fctriPad4:.clusterid.fctr3
## -2.4003871462
## sold.fctr sold.fctr.predict.All.X.Train.glmnet.N
## 1 N 831
## 2 Y 138
## sold.fctr.predict.All.X.Train.glmnet.Y
## 1 164
## 2 717
## Prediction
## Reference N Y
## N 831 164
## Y 138 717
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.367568e-01 6.723340e-01 8.191140e-01 8.533244e-01 5.378378e-01
## AccuracyPValue McnemarPValue
## 7.369741e-163 1.502672e-01
## id
## 1 All.X.Train.glmnet
## feats
## 1 biddable,sprice.d20nexp,spdiff.cut.fctr,D.ratio.wrds.stop.n.wrds.n,startprice.dcm2.is9,D.wrds.stop.n.log,D.weight.sum.stem.stop.Ratio,D.ratio.weight.sum.wrds.n,.rnorm,startprice.dcm1.is9,storage.fctr,D.chrs.pnct11.n.log,D.chrs.pnct13.n.log,startprice.dgt2.is9,color.fctr,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,prdl.my.fctr,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.n.log,D.terms.post.stem.n.log,D.wrds.unq.n.log,D.terms.post.stop.n.log,cellular.fctr,startprice.dgt1.is9,condition.fctr,sprice.log10,sprice.root2,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 19.916 1.963
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8279468 0.9015075 0.754386 0.908196
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.8260369 0.8203656
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.819114 0.8533244 0.6357114
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01119409 0.02374578## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.3## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.3
## [1] "fitting model: Max.cor.Y.rcv.1X1.Train.glmnet"
## [1] " indep_vars: biddable,sprice.root2"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.118 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdlIdPfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdlIdPfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda
## Length Class Mode
## a0 53 -none- numeric
## beta 106 dgCMatrix S4
## df 53 -none- numeric
## dim 2 -none- numeric
## lambda 53 -none- numeric
## dev.ratio 53 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 2 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) biddable sprice.root2
## 0.08599829 0.96405413 -0.05275067
## [1] "max lambda < lambdaOpt:"
## (Intercept) biddable sprice.root2
## 0.1315301 1.0274253 -0.0585648
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.1X1.Train.glmnet.N
## 1 N 831
## 2 Y 233
## sold.fctr.predict.Max.cor.Y.rcv.1X1.Train.glmnet.Y
## 1 164
## 2 622
## Prediction
## Reference N Y
## N 831 164
## Y 233 622
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.854054e-01 5.658740e-01 7.659824e-01 8.039192e-01 5.378378e-01
## AccuracyPValue McnemarPValue
## 7.167068e-109 6.429388e-04
## id feats max.nTuningRuns
## 1 Max.cor.Y.rcv.1X1.Train.glmnet biddable,sprice.root2 25
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 3.461 0.022 0.7813306
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.8351759 0.7274854 0.8429857 0.5
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.7580743 0.7854082 0.7659824
## max.AccuracyUpper.fit max.Kappa.fit max.AccuracySD.fit max.KappaSD.fit
## 1 0.8039192 0.5660779 0.007643858 0.01631135## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.4## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.4
## [1] "fitting model: Max.cor.Y.rcv.5X5.Train.glmnet"
## [1] " indep_vars: biddable,sprice.root2"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.118 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdlIdPfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdlIdPfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda
## Length Class Mode
## a0 53 -none- numeric
## beta 106 dgCMatrix S4
## df 53 -none- numeric
## dim 2 -none- numeric
## lambda 53 -none- numeric
## dev.ratio 53 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 2 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) biddable sprice.root2
## 0.08599829 0.96405413 -0.05275067
## [1] "max lambda < lambdaOpt:"
## (Intercept) biddable sprice.root2
## 0.1315301 1.0274253 -0.0585648
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.5X5.Train.glmnet.N
## 1 N 831
## 2 Y 233
## sold.fctr.predict.Max.cor.Y.rcv.5X5.Train.glmnet.Y
## 1 164
## 2 622
## Prediction
## Reference N Y
## N 831 164
## Y 233 622
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.854054e-01 5.658740e-01 7.659824e-01 8.039192e-01 5.378378e-01
## AccuracyPValue McnemarPValue
## 7.167068e-109 6.429388e-04
## id feats max.nTuningRuns
## 1 Max.cor.Y.rcv.5X5.Train.glmnet biddable,sprice.root2 25
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 3.133 0.022 0.7813306
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.8351759 0.7274854 0.8429857 0.5
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.7580743 0.7854082 0.7659824
## max.AccuracyUpper.fit max.Kappa.fit max.AccuracySD.fit max.KappaSD.fit
## 1 0.8039192 0.5660779 0.007643858 0.01631135## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.4## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.4
## [1] "fitting model: Max.cor.Y.rcv.5X1.Train.glmnet"
## [1] " indep_vars: biddable,sprice.root2"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.118 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdlIdPfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdlIdPfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda
## Length Class Mode
## a0 53 -none- numeric
## beta 106 dgCMatrix S4
## df 53 -none- numeric
## dim 2 -none- numeric
## lambda 53 -none- numeric
## dev.ratio 53 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 2 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) biddable sprice.root2
## 0.08599829 0.96405413 -0.05275067
## [1] "max lambda < lambdaOpt:"
## (Intercept) biddable sprice.root2
## 0.1315301 1.0274253 -0.0585648
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.5X1.Train.glmnet.N
## 1 N 831
## 2 Y 233
## sold.fctr.predict.Max.cor.Y.rcv.5X1.Train.glmnet.Y
## 1 164
## 2 622
## Prediction
## Reference N Y
## N 831 164
## Y 233 622
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.854054e-01 5.658740e-01 7.659824e-01 8.039192e-01 5.378378e-01
## AccuracyPValue McnemarPValue
## 7.167068e-109 6.429388e-04
## id feats max.nTuningRuns
## 1 Max.cor.Y.rcv.5X1.Train.glmnet biddable,sprice.root2 25
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 3.086 0.022 0.7813306
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.8351759 0.7274854 0.8429857 0.5
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.7580743 0.7854082 0.7659824
## max.AccuracyUpper.fit max.Kappa.fit max.AccuracySD.fit max.KappaSD.fit
## 1 0.8039192 0.5660779 0.007643858 0.01631135## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.4## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.4
## [1] "fitting model: Max.cor.Y.rcv.5X3.Train.glmnet"
## [1] " indep_vars: biddable,sprice.root2"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.118 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdlIdPfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdlIdPfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda
## Length Class Mode
## a0 53 -none- numeric
## beta 106 dgCMatrix S4
## df 53 -none- numeric
## dim 2 -none- numeric
## lambda 53 -none- numeric
## dev.ratio 53 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 2 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) biddable sprice.root2
## 0.08599829 0.96405413 -0.05275067
## [1] "max lambda < lambdaOpt:"
## (Intercept) biddable sprice.root2
## 0.1315301 1.0274253 -0.0585648
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.5X3.Train.glmnet.N
## 1 N 831
## 2 Y 233
## sold.fctr.predict.Max.cor.Y.rcv.5X3.Train.glmnet.Y
## 1 164
## 2 622
## Prediction
## Reference N Y
## N 831 164
## Y 233 622
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.854054e-01 5.658740e-01 7.659824e-01 8.039192e-01 5.378378e-01
## AccuracyPValue McnemarPValue
## 7.167068e-109 6.429388e-04
## id feats max.nTuningRuns
## 1 Max.cor.Y.rcv.5X3.Train.glmnet biddable,sprice.root2 25
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 3.513 0.023 0.7813306
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.8351759 0.7274854 0.8429857 0.5
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.7580743 0.7854082 0.7659824
## max.AccuracyUpper.fit max.Kappa.fit max.AccuracySD.fit max.KappaSD.fit
## 1 0.8039192 0.5660779 0.007643858 0.01631135## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.4## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.4
## [1] "fitting model: Max.cor.Y.rcv.3X5.Train.glmnet"
## [1] " indep_vars: biddable,sprice.root2"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.118 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdlIdPfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdlIdPfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda
## Length Class Mode
## a0 53 -none- numeric
## beta 106 dgCMatrix S4
## df 53 -none- numeric
## dim 2 -none- numeric
## lambda 53 -none- numeric
## dev.ratio 53 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 2 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) biddable sprice.root2
## 0.08599829 0.96405413 -0.05275067
## [1] "max lambda < lambdaOpt:"
## (Intercept) biddable sprice.root2
## 0.1315301 1.0274253 -0.0585648
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.3X5.Train.glmnet.N
## 1 N 831
## 2 Y 233
## sold.fctr.predict.Max.cor.Y.rcv.3X5.Train.glmnet.Y
## 1 164
## 2 622
## Prediction
## Reference N Y
## N 831 164
## Y 233 622
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.854054e-01 5.658740e-01 7.659824e-01 8.039192e-01 5.378378e-01
## AccuracyPValue McnemarPValue
## 7.167068e-109 6.429388e-04
## id feats max.nTuningRuns
## 1 Max.cor.Y.rcv.3X5.Train.glmnet biddable,sprice.root2 25
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 2.916 0.024 0.7813306
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.8351759 0.7274854 0.8429857 0.5
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.7580743 0.7854082 0.7659824
## max.AccuracyUpper.fit max.Kappa.fit max.AccuracySD.fit max.KappaSD.fit
## 1 0.8039192 0.5660779 0.007643858 0.01631135## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.4## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.4
## [1] "fitting model: RFE.X.Train.glmnet"
## [1] " indep_vars: sprice.d20nexp,sprice.log10,sprice.root2,biddable,spdiff.cut.fctr,D.ratio.wrds.stop.n.wrds.n,cellular.fctr,storage.fctr,condition.fctr,prdl.my.fctr,startprice.dcm2.is9,D.weight.sum.stem.stop.Ratio,color.fctr,.rnorm,D.wrds.stop.n.log,startprice.dcm1.is9,D.chrs.pnct11.n.log,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.chrs.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.00548 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdlIdPfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha
## Length Class Mode
## a0 85 -none- numeric
## beta 7905 dgCMatrix S4
## df 85 -none- numeric
## dim 2 -none- numeric
## lambda 85 -none- numeric
## dev.ratio 85 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 93 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## -0.5748424332
## D.chrs.pnct13.n.log
## -0.0154166095
## D.ratio.weight.sum.wrds.n
## -0.0004393607
## D.terms.post.stop.n.log
## -0.0089438359
## biddable
## 1.9394377386
## cellular.fctr1
## 0.0702567191
## cellular.fctrUnknown
## -0.1320135490
## color.fctrWhite
## -0.0266151806
## condition.fctrFor parts or not working
## -0.2200229713
## condition.fctrNew other (see details)
## 0.4387934720
## condition.fctrSeller refurbished
## -0.4201823354
## prdl.my.fctriPad1
## -0.4548555560
## prdl.my.fctriPad2
## -0.3245612053
## prdl.my.fctriPad4
## 0.1977145978
## prdl.my.fctriPadAir
## 0.6128927100
## prdl.my.fctriPadAir2
## 0.8968352444
## prdl.my.fctriPadmini
## -0.1622395269
## prdl.my.fctriPadmini2
## 0.1674866134
## spdiff.cut.fctr(-100,-10]
## 1.1267058623
## spdiff.cut.fctr(-10,-1]
## 2.6792229681
## spdiff.cut.fctr(-1,0]
## 3.6061648196
## spdiff.cut.fctr(0,1]
## 2.7445701555
## spdiff.cut.fctr(1,10]
## 2.4590410900
## spdiff.cut.fctr(10,100]
## 2.0111381095
## spdiff.cut.fctr(100,1e+03]
## 0.2412671988
## sprice.log10
## -0.1516075270
## sprice.root2
## -0.0864776262
## startprice.dcm1.is9
## -0.2865501177
## storage.fctr32
## -0.0032900963
## storage.fctr64
## 0.2388740478
## cellular.fctr1:carrier.fctrSprint
## 0.1795038991
## cellular.fctr1:carrier.fctrT-Mobile
## 0.0553779569
## cellular.fctrUnknown:carrier.fctrUnknown
## -0.0076892795
## prdl.my.fctrUnknown:.clusterid.fctr2
## 0.3891983721
## prdl.my.fctriPad1:.clusterid.fctr2
## -0.4118967518
## prdl.my.fctriPad3:.clusterid.fctr2
## -0.2066322688
## prdl.my.fctriPad1:.clusterid.fctr3
## -0.7279651218
## prdl.my.fctriPad4:.clusterid.fctr3
## -2.2439690983
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## -0.4887481635
## D.chrs.pnct11.n.log
## -0.0049897588
## D.chrs.pnct13.n.log
## -0.0343205066
## D.ratio.weight.sum.wrds.n
## -0.0065169343
## D.terms.post.stop.n.log
## -0.0039264020
## D.weight.sum.stem.stop.Ratio
## -0.1489884401
## biddable
## 1.9631938033
## cellular.fctr1
## 0.0786061208
## cellular.fctrUnknown
## -0.1412878868
## color.fctrWhite
## -0.0385898938
## condition.fctrFor parts or not working
## -0.2344565867
## condition.fctrNew other (see details)
## 0.4627975233
## condition.fctrSeller refurbished
## -0.4454800069
## prdl.my.fctriPad1
## -0.4810735152
## prdl.my.fctriPad2
## -0.3463142796
## prdl.my.fctriPad4
## 0.2270111894
## prdl.my.fctriPadAir
## 0.6385121785
## prdl.my.fctriPadAir2
## 0.9150943539
## prdl.my.fctriPadmini
## -0.1801302700
## prdl.my.fctriPadmini2
## 0.1762468108
## spdiff.cut.fctr(-100,-10]
## 1.2080758350
## spdiff.cut.fctr(-10,-1]
## 2.7783245058
## spdiff.cut.fctr(-1,0]
## 3.7489075715
## spdiff.cut.fctr(0,1]
## 2.8862595318
## spdiff.cut.fctr(1,10]
## 2.5615257475
## spdiff.cut.fctr(10,100]
## 2.0983774318
## spdiff.cut.fctr(100,1e+03]
## 0.3228440594
## sprice.log10
## -0.1541621517
## sprice.root2
## -0.0866668571
## startprice.dcm1.is9
## -0.2961675551
## storage.fctr32
## -0.0092455272
## storage.fctr64
## 0.2513803695
## cellular.fctr1:carrier.fctrSprint
## 0.2045698195
## cellular.fctr1:carrier.fctrT-Mobile
## 0.0719134607
## cellular.fctrUnknown:carrier.fctrUnknown
## -0.0119069939
## prdl.my.fctrUnknown:.clusterid.fctr2
## 0.4302836448
## prdl.my.fctriPad1:.clusterid.fctr2
## -0.4315400934
## prdl.my.fctriPad3:.clusterid.fctr2
## -0.2494241814
## prdl.my.fctrUnknown:.clusterid.fctr3
## -0.0005985027
## prdl.my.fctriPad1:.clusterid.fctr3
## -0.7691702044
## prdl.my.fctriPad4:.clusterid.fctr3
## -2.4003871462
## sold.fctr sold.fctr.predict.RFE.X.Train.glmnet.N
## 1 N 831
## 2 Y 138
## sold.fctr.predict.RFE.X.Train.glmnet.Y
## 1 164
## 2 717
## Prediction
## Reference N Y
## N 831 164
## Y 138 717
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.367568e-01 6.723340e-01 8.191140e-01 8.533244e-01 5.378378e-01
## AccuracyPValue McnemarPValue
## 7.369741e-163 1.502672e-01
## id
## 1 RFE.X.Train.glmnet
## feats
## 1 sprice.d20nexp,sprice.log10,sprice.root2,biddable,spdiff.cut.fctr,D.ratio.wrds.stop.n.wrds.n,cellular.fctr,storage.fctr,condition.fctr,prdl.my.fctr,startprice.dcm2.is9,D.weight.sum.stem.stop.Ratio,color.fctr,.rnorm,D.wrds.stop.n.log,startprice.dcm1.is9,D.chrs.pnct11.n.log,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.chrs.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 18.923 1.876
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8279468 0.9015075 0.754386 0.908196
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.8260369 0.8203656
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.819114 0.8533244 0.6357114
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01119409 0.02374578## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.3## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.3
## [1] "fitting model: Max.cor.Y.rcv.3X3.Train.glmnet"
## [1] " indep_vars: biddable,sprice.root2"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.118 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdlIdPfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdlIdPfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda
## Length Class Mode
## a0 53 -none- numeric
## beta 106 dgCMatrix S4
## df 53 -none- numeric
## dim 2 -none- numeric
## lambda 53 -none- numeric
## dev.ratio 53 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 2 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) biddable sprice.root2
## 0.08599829 0.96405413 -0.05275067
## [1] "max lambda < lambdaOpt:"
## (Intercept) biddable sprice.root2
## 0.1315301 1.0274253 -0.0585648
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.3X3.Train.glmnet.N
## 1 N 831
## 2 Y 233
## sold.fctr.predict.Max.cor.Y.rcv.3X3.Train.glmnet.Y
## 1 164
## 2 622
## Prediction
## Reference N Y
## N 831 164
## Y 233 622
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.854054e-01 5.658740e-01 7.659824e-01 8.039192e-01 5.378378e-01
## AccuracyPValue McnemarPValue
## 7.167068e-109 6.429388e-04
## id feats max.nTuningRuns
## 1 Max.cor.Y.rcv.3X3.Train.glmnet biddable,sprice.root2 25
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 3.092 0.021 0.7813306
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.8351759 0.7274854 0.8429857 0.5
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.7580743 0.7854082 0.7659824
## max.AccuracyUpper.fit max.Kappa.fit max.AccuracySD.fit max.KappaSD.fit
## 1 0.8039192 0.5660779 0.007643858 0.01631135## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.4## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.4## [1] "fitting model: CSM.X.spatialSign.Train.bagEarth"
## [1] " indep_vars: spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr"
## + : degree=1, nprune=64## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## - : degree=1, nprune=64
## Aggregating results
## Fitting final model on full training set## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
##
## Call:
## bagEarth.default(x = structure(c(-0.480112420809766, 1.43504225194153, 2.52526248536899, 0.927227061458224, -1.6375712221881, -0.89892418133905, -1.09536634163365, 0.367913716767642, 1.54830639736751, -0.368003285764661, 0.594873558861309, -0.528788276883531, -0.482448574433828, -1.23003696188636, -0.458778254303339, 0.294185036488187, 0.895917830346375, 1.27864282766749, 1.18804461070629, -0.521798717011069, 1.559931581346, 1.09047221934056, -0.91850254102916, 1.87462218220423, -0.677831780588588, -0.816487211016767, -0.00698246281289809, 0.0810553378009752, 1.03341902803144, -0.81757098374442, 1.1714905965126, -1.66333330096743, -0.503451670272392, 0.79492183444965, -0.178042935719029, -0.914308401049965, 0.413520165469343, -1.75677234237956, -0.384349130016673, 0.031671391224914, 1.16314925979891, -0.77802211523859, -1.26735911622058, -1.96631758040354, -0.117360853100685, 0.0738291506769477, -1.0538922620204, 0.546280137629045, 1.01242920728111, -0.848743614154288, -0.0332114518512125, 0.999685015258156, 0.636598885177834, 0.024788868649861, 0.00852869176320631, -0.818982399239164, 0.379500784049766, -0.680825135178795, 0.521448153419782, -1.26460271751457, 1.36007418916595, -1.03227718276357, 0.654671817557307, -0.883918863763299, -0.693984880518334, 0.647143692392904, 0.919226908582389, 0.575289341271266, 1.08812088345302, -0.743052878778297, -0.244494683584288, -0.137496510308379, 0.829029718047623, -0.725294091780842, -1.68309162582624, 0.785335863072958, -0.553209374946575, -0.559644955122013, 1.1842966803235, 1.26804042101133, 0.422970906063789, 1.89595290373643, 0.336755545604089, 1.42493068703692, -0.864669104597755, 1.62891319221593, -0.368353606725079, -0.894664711048527, -1.13078821881385, -1.92380754137579, 0.388390898592933, 0.831862139220842, -0.0405705287435537, -0.510790909305918, -0.349402834271897, 0.915724672603292, 0.269662224097122, 0.232631506860945, -0.787530354605316, 0.229586925162045, -0.246803992534046, 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"1849", "1850")), degree = 1, nprune = 64, keepxy = TRUE, glm = structure(list( family = function (link = "logit") { linktemp <- substitute(link) if (!is.character(linktemp)) linktemp <- deparse(linktemp) okLinks <- c("logit", "probit", "cloglog", "cauchit", "log") if (linktemp %in% okLinks) stats <- make.link(linktemp) else if (is.character(link)) { stats <- make.link(link) linktemp <- link } else { if (inherits(link, "link-glm")) { stats <- link if (!is.null(stats$name)) linktemp <- stats$name } else { stop(gettextf("link \"%s\" not available for binomial family; available links are %s", linktemp, paste(sQuote(okLinks), collapse = ", ")), domain = NA) } } variance <- function(mu) mu * (1 - mu) validmu <- function(mu) all(is.finite(mu)) && all(mu > 0 & mu < 1) dev.resids <- function(y, mu, wt) .Call(C_binomial_dev_resids, y, mu, wt) aic <- function(y, n, mu, wt, dev) { m <- if (any(n > 1)) n else wt -2 * sum(ifelse(m > 0, (wt/m), 0) * dbinom(round(m * y), round(m), mu, log = TRUE)) } initialize <- expression({ if (NCOL(y) == 1) { if (is.factor(y)) y <- y != levels(y)[1L] n <- rep.int(1, nobs) y[weights == 0] <- 0 if (any(y < 0 | y > 1)) stop("y values must be 0 <= y <= 1") mustart <- (weights * y + 0.5)/(weights + 1) m <- weights * y if (any(abs(m - round(m)) > 0.001)) warning("non-integer #successes in a binomial glm!") } else if (NCOL(y) == 2) { if (any(abs(y - round(y)) > 0.001)) warning("non-integer counts in a binomial glm!") n <- y[, 1] + y[, 2] y <- ifelse(n == 0, 0, y[, 1]/n) weights <- weights * n mustart <- (n * y + 0.5)/(n + 1) } else stop("for the 'binomial' family, y must be a vector of 0 and 1's\nor a 2 column matrix where col 1 is no. successes and col 2 is no. failures") }) simfun <- function(object, nsim) { ftd <- fitted(object) n <- length(ftd) ntot <- n * nsim wts <- object$prior.weights if (any(wts%%1 != 0)) stop("cannot simulate from non-integer prior.weights") if (!is.null(m <- object$model)) { y <- model.response(m) if (is.factor(y)) { yy <- factor(1 + rbinom(ntot, size = 1, prob = ftd), labels = levels(y)) split(yy, rep(seq_len(nsim), each = n)) } else if (is.matrix(y) && ncol(y) == 2) { yy <- vector("list", nsim) for (i in seq_len(nsim)) { Y <- rbinom(n, size = wts, prob = ftd) YY <- cbind(Y, wts - Y) colnames(YY) <- colnames(y) yy[[i]] <- YY } yy } else rbinom(ntot, size = wts, prob = ftd)/wts } else rbinom(ntot, size = wts, prob = ftd)/wts } structure(list(family = "binomial", link = linktemp, linkfun = stats$linkfun, linkinv = stats$linkinv, variance = variance, dev.resids = dev.resids, aic = aic, mu.eta = stats$mu.eta, initialize = initialize, validmu = validmu, valideta = stats$valideta, simulate = simfun), class = "family") }), .Names = "family"))
##
## Out of bag statistics:
##
## Accuracy Kappa
## 0% 0.7771 0.5520
## 2.5% 0.7850 0.5650
## 25% 0.8007 0.5991
## 50% 0.8082 0.6144
## 75% 0.8184 0.6320
## 97.5% 0.8322 0.6586
## 100% 0.8447 0.6829
##
## Model Selection Statistics:
##
## Num Terms Num Variables
## Length:1563 Length:1563
## Class :character Class :character
## Mode :character Mode :character
## sold.fctr sold.fctr.predict.CSM.X.spatialSign.Train.bagEarth.N
## 1 N 778
## 2 Y 75
## sold.fctr.predict.CSM.X.spatialSign.Train.bagEarth.Y
## 1 217
## 2 780
## Prediction
## Reference N Y
## N 778 217
## Y 75 780
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.421622e-01 6.861729e-01 8.247380e-01 8.584936e-01 5.378378e-01
## AccuracyPValue McnemarPValue
## 2.200963e-169 1.565485e-16
## id
## 1 CSM.X.spatialSign.Train.bagEarth
## feats
## 1 spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 148.257 74.293
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8547651 0.9025126 0.8070175 0.9340316
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.3 0.8423326 0.8108098
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.824738 0.8584936 0.6163134## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.4## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.4
## [1] "fitting model: Max.cor.Y.rcv.3X1.Train.glmnet"
## [1] " indep_vars: biddable,sprice.root2"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.118 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdlIdPfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdlIdPfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda
## Length Class Mode
## a0 53 -none- numeric
## beta 106 dgCMatrix S4
## df 53 -none- numeric
## dim 2 -none- numeric
## lambda 53 -none- numeric
## dev.ratio 53 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 2 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) biddable sprice.root2
## 0.08599829 0.96405413 -0.05275067
## [1] "max lambda < lambdaOpt:"
## (Intercept) biddable sprice.root2
## 0.1315301 1.0274253 -0.0585648
## sold.fctr sold.fctr.predict.Max.cor.Y.rcv.3X1.Train.glmnet.N
## 1 N 831
## 2 Y 233
## sold.fctr.predict.Max.cor.Y.rcv.3X1.Train.glmnet.Y
## 1 164
## 2 622
## Prediction
## Reference N Y
## N 831 164
## Y 233 622
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.854054e-01 5.658740e-01 7.659824e-01 8.039192e-01 5.378378e-01
## AccuracyPValue McnemarPValue
## 7.167068e-109 6.429388e-04
## id feats max.nTuningRuns
## 1 Max.cor.Y.rcv.3X1.Train.glmnet biddable,sprice.root2 25
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 2.963 0.026 0.7813306
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.8351759 0.7274854 0.8429857 0.5
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.7580743 0.7854082 0.7659824
## max.AccuracyUpper.fit max.Kappa.fit max.AccuracySD.fit max.KappaSD.fit
## 1 0.8039192 0.5660779 0.007643858 0.01631135## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.4## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.4## [1] "fitting model: RFE.X.Train.glm"
## [1] " indep_vars: sprice.d20nexp,sprice.log10,sprice.root2,biddable,spdiff.cut.fctr,D.ratio.wrds.stop.n.wrds.n,cellular.fctr,storage.fctr,condition.fctr,prdl.my.fctr,startprice.dcm2.is9,D.weight.sum.stem.stop.Ratio,color.fctr,.rnorm,D.wrds.stop.n.log,startprice.dcm1.is9,D.chrs.pnct11.n.log,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.chrs.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr"
## + Fold1.Rep1: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold1.Rep1: parameter=none
## + Fold2.Rep1: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold2.Rep1: parameter=none
## + Fold3.Rep1: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold3.Rep1: parameter=none
## + Fold1.Rep2: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold1.Rep2: parameter=none
## + Fold2.Rep2: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold2.Rep2: parameter=none
## + Fold3.Rep2: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold3.Rep2: parameter=none
## + Fold1.Rep3: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold1.Rep3: parameter=none
## + Fold2.Rep3: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold2.Rep3: parameter=none
## + Fold3.Rep3: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold3.Rep3: parameter=none
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.9681 -0.5779 -0.1363 0.4125 3.2961
##
## Coefficients: (26 not defined because of singularities)
## Estimate Std. Error z value
## (Intercept) 4.818e+00 1.980e+01 0.243
## .rnorm -2.241e-02 7.036e-02 -0.319
## D.chrs.n.log -1.851e+00 1.687e+00 -1.097
## D.chrs.pnct11.n.log -1.113e-01 2.756e-01 -0.404
## D.chrs.pnct13.n.log -2.204e-01 2.538e-01 -0.868
## D.chrs.uppr.n.log 2.824e-01 1.511e+00 0.187
## D.ratio.weight.sum.wrds.n -6.955e-03 2.420e-01 -0.029
## D.ratio.wrds.stop.n.wrds.n -6.235e+00 2.607e+00 -2.392
## D.terms.post.stem.n.log 1.221e+01 6.344e+00 1.925
## D.terms.post.stop.n.log -1.692e+01 6.386e+00 -2.649
## D.weight.post.stem.sum -1.434e+00 3.556e+00 -0.403
## D.weight.post.stop.sum 1.288e+00 3.448e+00 0.374
## D.weight.sum NA NA NA
## D.weight.sum.stem.stop.Ratio 2.281e+00 1.993e+01 0.114
## D.wrds.n.log 5.303e+00 1.614e+00 3.285
## D.wrds.stop.n.log 2.504e-01 4.553e-01 0.550
## D.wrds.unq.n.log NA NA NA
## biddable 2.218e+00 1.932e-01 11.478
## cellular.fctr1 1.632e-01 2.387e-01 0.684
## cellular.fctrUnknown -5.645e-01 3.629e-01 -1.555
## color.fctrGold -2.126e-01 4.745e-01 -0.448
## `color.fctrSpace Gray` -9.572e-03 2.867e-01 -0.033
## color.fctrUnknown -7.209e-02 1.980e-01 -0.364
## color.fctrWhite -1.971e-01 2.186e-01 -0.901
## `condition.fctrFor parts or not working` -6.153e-01 2.959e-01 -2.079
## `condition.fctrManufacturer refurbished` -2.487e-01 5.034e-01 -0.494
## condition.fctrNew 1.134e-01 2.671e-01 0.425
## `condition.fctrNew other (see details)` 6.755e-01 3.804e-01 1.776
## `condition.fctrSeller refurbished` -7.642e-01 3.297e-01 -2.318
## prdl.my.fctriPad1 -8.169e-01 4.361e-01 -1.873
## prdl.my.fctriPad2 -4.572e-01 3.849e-01 -1.188
## prdl.my.fctriPad3 1.912e-01 4.248e-01 0.450
## prdl.my.fctriPad4 8.521e-01 4.467e-01 1.908
## prdl.my.fctriPadAir 1.231e+00 4.419e-01 2.786
## prdl.my.fctriPadAir2 1.508e+00 4.973e-01 3.032
## prdl.my.fctriPadmini -3.018e-01 3.801e-01 -0.794
## prdl.my.fctriPadmini2 5.812e-01 4.593e-01 1.265
## prdl.my.fctriPadmini3 4.597e-01 5.288e-01 0.869
## `spdiff.cut.fctr(-100,-10]` 2.286e+00 2.845e-01 8.035
## `spdiff.cut.fctr(-10,-1]` 4.108e+00 3.673e-01 11.184
## `spdiff.cut.fctr(-1,0]` 5.699e+00 7.320e-01 7.786
## `spdiff.cut.fctr(0,1]` 4.867e+00 8.097e-01 6.012
## `spdiff.cut.fctr(1,10]` 3.920e+00 3.762e-01 10.419
## `spdiff.cut.fctr(10,100]` 3.253e+00 3.340e-01 9.739
## `spdiff.cut.fctr(100,1e+03]` 1.174e+00 5.228e-01 2.246
## sprice.d20nexp -1.820e+00 1.815e+00 -1.002
## sprice.log10 -5.453e-01 5.075e-01 -1.074
## sprice.root2 -8.693e-02 7.844e-02 -1.108
## startprice.dcm1.is9 -6.420e-01 2.678e-01 -2.397
## startprice.dcm2.is9 1.896e-01 2.660e-01 0.713
## startprice.dgt1.is9 7.679e-02 1.880e-01 0.409
## startprice.dgt2.is9 6.693e-02 2.192e-01 0.305
## storage.fctr16 -7.430e-01 4.403e-01 -1.687
## storage.fctr32 -7.296e-01 4.546e-01 -1.605
## storage.fctr64 -1.910e-01 4.343e-01 -0.440
## storage.fctrUnknown -9.796e-02 5.593e-01 -0.175
## `cellular.fctr0:carrier.fctrNone` NA NA NA
## `cellular.fctr1:carrier.fctrNone` NA NA NA
## `cellular.fctrUnknown:carrier.fctrNone` NA NA NA
## `cellular.fctr0:carrier.fctrOther` NA NA NA
## `cellular.fctr1:carrier.fctrOther` 1.364e+01 1.691e+03 0.008
## `cellular.fctrUnknown:carrier.fctrOther` NA NA NA
## `cellular.fctr0:carrier.fctrSprint` NA NA NA
## `cellular.fctr1:carrier.fctrSprint` 4.476e-01 5.582e-01 0.802
## `cellular.fctrUnknown:carrier.fctrSprint` NA NA NA
## `cellular.fctr0:carrier.fctrT-Mobile` NA NA NA
## `cellular.fctr1:carrier.fctrT-Mobile` 2.231e-01 6.784e-01 0.329
## `cellular.fctrUnknown:carrier.fctrT-Mobile` NA NA NA
## `cellular.fctr0:carrier.fctrUnknown` NA NA NA
## `cellular.fctr1:carrier.fctrUnknown` -3.460e-02 4.030e-01 -0.086
## `cellular.fctrUnknown:carrier.fctrUnknown` NA NA NA
## `cellular.fctr0:carrier.fctrVerizon` NA NA NA
## `cellular.fctr1:carrier.fctrVerizon` 1.447e-01 3.391e-01 0.427
## `cellular.fctrUnknown:carrier.fctrVerizon` NA NA NA
## `prdl.my.fctrUnknown:.clusterid.fctr2` 8.204e-01 6.142e-01 1.336
## `prdl.my.fctriPad1:.clusterid.fctr2` -1.065e+00 5.999e-01 -1.776
## `prdl.my.fctriPad2:.clusterid.fctr2` NA NA NA
## `prdl.my.fctriPad3:.clusterid.fctr2` -8.253e-01 7.431e-01 -1.111
## `prdl.my.fctriPad4:.clusterid.fctr2` -2.964e-01 7.953e-01 -0.373
## `prdl.my.fctriPadAir:.clusterid.fctr2` 4.084e-01 5.991e-01 0.682
## `prdl.my.fctriPadAir2:.clusterid.fctr2` NA NA NA
## `prdl.my.fctriPadmini:.clusterid.fctr2` NA NA NA
## `prdl.my.fctriPadmini2:.clusterid.fctr2` -6.078e-01 8.755e-01 -0.694
## `prdl.my.fctriPadmini3:.clusterid.fctr2` NA NA NA
## `prdl.my.fctrUnknown:.clusterid.fctr3` 6.544e-02 8.082e-01 0.081
## `prdl.my.fctriPad1:.clusterid.fctr3` -1.499e+00 7.064e-01 -2.121
## `prdl.my.fctriPad2:.clusterid.fctr3` NA NA NA
## `prdl.my.fctriPad3:.clusterid.fctr3` NA NA NA
## `prdl.my.fctriPad4:.clusterid.fctr3` -1.618e+01 3.993e+02 -0.041
## `prdl.my.fctriPadAir:.clusterid.fctr3` NA NA NA
## `prdl.my.fctriPadAir2:.clusterid.fctr3` NA NA NA
## `prdl.my.fctriPadmini:.clusterid.fctr3` NA NA NA
## `prdl.my.fctriPadmini2:.clusterid.fctr3` NA NA NA
## `prdl.my.fctriPadmini3:.clusterid.fctr3` NA NA NA
## Pr(>|z|)
## (Intercept) 0.80769
## .rnorm 0.75004
## D.chrs.n.log 0.27271
## D.chrs.pnct11.n.log 0.68629
## D.chrs.pnct13.n.log 0.38525
## D.chrs.uppr.n.log 0.85178
## D.ratio.weight.sum.wrds.n 0.97708
## D.ratio.wrds.stop.n.wrds.n 0.01676 *
## D.terms.post.stem.n.log 0.05428 .
## D.terms.post.stop.n.log 0.00807 **
## D.weight.post.stem.sum 0.68672
## D.weight.post.stop.sum 0.70870
## D.weight.sum NA
## D.weight.sum.stem.stop.Ratio 0.90889
## D.wrds.n.log 0.00102 **
## D.wrds.stop.n.log 0.58241
## D.wrds.unq.n.log NA
## biddable < 2e-16 ***
## cellular.fctr1 0.49420
## cellular.fctrUnknown 0.11987
## color.fctrGold 0.65405
## `color.fctrSpace Gray` 0.97336
## color.fctrUnknown 0.71579
## color.fctrWhite 0.36733
## `condition.fctrFor parts or not working` 0.03760 *
## `condition.fctrManufacturer refurbished` 0.62137
## condition.fctrNew 0.67100
## `condition.fctrNew other (see details)` 0.07575 .
## `condition.fctrSeller refurbished` 0.02045 *
## prdl.my.fctriPad1 0.06103 .
## prdl.my.fctriPad2 0.23493
## prdl.my.fctriPad3 0.65259
## prdl.my.fctriPad4 0.05642 .
## prdl.my.fctriPadAir 0.00533 **
## prdl.my.fctriPadAir2 0.00243 **
## prdl.my.fctriPadmini 0.42718
## prdl.my.fctriPadmini2 0.20571
## prdl.my.fctriPadmini3 0.38471
## `spdiff.cut.fctr(-100,-10]` 9.38e-16 ***
## `spdiff.cut.fctr(-10,-1]` < 2e-16 ***
## `spdiff.cut.fctr(-1,0]` 6.93e-15 ***
## `spdiff.cut.fctr(0,1]` 1.84e-09 ***
## `spdiff.cut.fctr(1,10]` < 2e-16 ***
## `spdiff.cut.fctr(10,100]` < 2e-16 ***
## `spdiff.cut.fctr(100,1e+03]` 0.02472 *
## sprice.d20nexp 0.31610
## sprice.log10 0.28263
## sprice.root2 0.26778
## startprice.dcm1.is9 0.01653 *
## startprice.dcm2.is9 0.47601
## startprice.dgt1.is9 0.68290
## startprice.dgt2.is9 0.76007
## storage.fctr16 0.09154 .
## storage.fctr32 0.10845
## storage.fctr64 0.66013
## storage.fctrUnknown 0.86096
## `cellular.fctr0:carrier.fctrNone` NA
## `cellular.fctr1:carrier.fctrNone` NA
## `cellular.fctrUnknown:carrier.fctrNone` NA
## `cellular.fctr0:carrier.fctrOther` NA
## `cellular.fctr1:carrier.fctrOther` 0.99357
## `cellular.fctrUnknown:carrier.fctrOther` NA
## `cellular.fctr0:carrier.fctrSprint` NA
## `cellular.fctr1:carrier.fctrSprint` 0.42261
## `cellular.fctrUnknown:carrier.fctrSprint` NA
## `cellular.fctr0:carrier.fctrT-Mobile` NA
## `cellular.fctr1:carrier.fctrT-Mobile` 0.74229
## `cellular.fctrUnknown:carrier.fctrT-Mobile` NA
## `cellular.fctr0:carrier.fctrUnknown` NA
## `cellular.fctr1:carrier.fctrUnknown` 0.93159
## `cellular.fctrUnknown:carrier.fctrUnknown` NA
## `cellular.fctr0:carrier.fctrVerizon` NA
## `cellular.fctr1:carrier.fctrVerizon` 0.66957
## `cellular.fctrUnknown:carrier.fctrVerizon` NA
## `prdl.my.fctrUnknown:.clusterid.fctr2` 0.18165
## `prdl.my.fctriPad1:.clusterid.fctr2` 0.07578 .
## `prdl.my.fctriPad2:.clusterid.fctr2` NA
## `prdl.my.fctriPad3:.clusterid.fctr2` 0.26669
## `prdl.my.fctriPad4:.clusterid.fctr2` 0.70943
## `prdl.my.fctriPadAir:.clusterid.fctr2` 0.49539
## `prdl.my.fctriPadAir2:.clusterid.fctr2` NA
## `prdl.my.fctriPadmini:.clusterid.fctr2` NA
## `prdl.my.fctriPadmini2:.clusterid.fctr2` 0.48751
## `prdl.my.fctriPadmini3:.clusterid.fctr2` NA
## `prdl.my.fctrUnknown:.clusterid.fctr3` 0.93547
## `prdl.my.fctriPad1:.clusterid.fctr3` 0.03389 *
## `prdl.my.fctriPad2:.clusterid.fctr3` NA
## `prdl.my.fctriPad3:.clusterid.fctr3` NA
## `prdl.my.fctriPad4:.clusterid.fctr3` 0.96768
## `prdl.my.fctriPadAir:.clusterid.fctr3` NA
## `prdl.my.fctriPadAir2:.clusterid.fctr3` NA
## `prdl.my.fctriPadmini:.clusterid.fctr3` NA
## `prdl.my.fctriPadmini2:.clusterid.fctr3` NA
## `prdl.my.fctriPadmini3:.clusterid.fctr3` NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2554.0 on 1849 degrees of freedom
## Residual deviance: 1329.2 on 1782 degrees of freedom
## AIC: 1465.2
##
## Number of Fisher Scoring iterations: 15## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## sold.fctr sold.fctr.predict.RFE.X.Train.glm.N
## 1 N 833
## 2 Y 138
## sold.fctr.predict.RFE.X.Train.glm.Y
## 1 162
## 2 717
## Prediction
## Reference N Y
## N 833 162
## Y 138 717
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.378378e-01 6.744505e-01 8.202383e-01 8.543588e-01 5.378378e-01
## AccuracyPValue McnemarPValue
## 3.770097e-164 1.842093e-01
## id
## 1 RFE.X.Train.glm
## feats
## 1 sprice.d20nexp,sprice.log10,sprice.root2,biddable,spdiff.cut.fctr,D.ratio.wrds.stop.n.wrds.n,cellular.fctr,storage.fctr,condition.fctr,prdl.my.fctr,startprice.dcm2.is9,D.weight.sum.stem.stop.Ratio,color.fctr,.rnorm,D.wrds.stop.n.log,startprice.dcm1.is9,D.chrs.pnct11.n.log,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.chrs.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 3.993 0.324
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8376238 0.8904523 0.7847953 0.9180828
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.8269896 0.8185584
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8202383 0.8543588 0.6330313
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01101111 0.0227628## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.5## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.5## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## [1] "fitting model: Max.cor.Y.Train.rpart"
## [1] " indep_vars: biddable,sprice.root2"
## + Fold1.Rep1: cp=0.001754
## - Fold1.Rep1: cp=0.001754
## + Fold2.Rep1: cp=0.001754
## - Fold2.Rep1: cp=0.001754
## + Fold3.Rep1: cp=0.001754
## - Fold3.Rep1: cp=0.001754
## + Fold1.Rep2: cp=0.001754
## - Fold1.Rep2: cp=0.001754
## + Fold2.Rep2: cp=0.001754
## - Fold2.Rep2: cp=0.001754
## + Fold3.Rep2: cp=0.001754
## - Fold3.Rep2: cp=0.001754
## + Fold1.Rep3: cp=0.001754
## - Fold1.Rep3: cp=0.001754
## + Fold2.Rep3: cp=0.001754
## - Fold2.Rep3: cp=0.001754
## + Fold3.Rep3: cp=0.001754
## - Fold3.Rep3: cp=0.001754
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.00351 on full training set 
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 1850
##
## CP nsplit rel error
## 1 0.515789474 0 1.0000000
## 2 0.011695906 1 0.4842105
## 3 0.004678363 3 0.4608187
## 4 0.003508772 7 0.4421053
##
## Variable importance
## biddable sprice.root2
## 58 42
##
## Node number 1: 1850 observations, complexity param=0.5157895
## predicted class=N expected loss=0.4621622 P(node) =1
## class counts: 995 855
## probabilities: 0.538 0.462
## left son=2 (1015 obs) right son=3 (835 obs)
## Primary splits:
## biddable < 0.5 to the left, improve=277.4446, (0 missing)
## sprice.root2 < 10.02494 to the right, improve=217.5035, (0 missing)
## Surrogate splits:
## sprice.root2 < 10.02494 to the right, agree=0.75, adj=0.447, (0 split)
##
## Node number 2: 1015 observations
## predicted class=N expected loss=0.2137931 P(node) =0.5486486
## class counts: 798 217
## probabilities: 0.786 0.214
##
## Node number 3: 835 observations, complexity param=0.01169591
## predicted class=Y expected loss=0.2359281 P(node) =0.4513514
## class counts: 197 638
## probabilities: 0.236 0.764
## left son=6 (293 obs) right son=7 (542 obs)
## Primary splits:
## sprice.root2 < 11.83195 to the right, improve=63.77111, (0 missing)
##
## Node number 6: 293 observations, complexity param=0.01169591
## predicted class=N expected loss=0.4982935 P(node) =0.1583784
## class counts: 147 146
## probabilities: 0.502 0.498
## left son=12 (38 obs) right son=13 (255 obs)
## Primary splits:
## sprice.root2 < 20.33187 to the right, improve=5.969295, (0 missing)
##
## Node number 7: 542 observations
## predicted class=Y expected loss=0.09225092 P(node) =0.292973
## class counts: 50 492
## probabilities: 0.092 0.908
##
## Node number 12: 38 observations
## predicted class=N expected loss=0.2368421 P(node) =0.02054054
## class counts: 29 9
## probabilities: 0.763 0.237
##
## Node number 13: 255 observations, complexity param=0.004678363
## predicted class=Y expected loss=0.4627451 P(node) =0.1378378
## class counts: 118 137
## probabilities: 0.463 0.537
## left son=26 (14 obs) right son=27 (241 obs)
## Primary splits:
## sprice.root2 < 12.24684 to the left, improve=0.9610958, (0 missing)
##
## Node number 26: 14 observations
## predicted class=N expected loss=0.3571429 P(node) =0.007567568
## class counts: 9 5
## probabilities: 0.643 0.357
##
## Node number 27: 241 observations, complexity param=0.004678363
## predicted class=Y expected loss=0.4522822 P(node) =0.1302703
## class counts: 109 132
## probabilities: 0.452 0.548
## left son=54 (233 obs) right son=55 (8 obs)
## Primary splits:
## sprice.root2 < 12.24724 to the right, improve=3.385322, (0 missing)
##
## Node number 54: 233 observations, complexity param=0.004678363
## predicted class=Y expected loss=0.4678112 P(node) =0.1259459
## class counts: 109 124
## probabilities: 0.468 0.532
## left son=108 (129 obs) right son=109 (104 obs)
## Primary splits:
## sprice.root2 < 14.35185 to the right, improve=0.7518126, (0 missing)
##
## Node number 55: 8 observations
## predicted class=Y expected loss=0 P(node) =0.004324324
## class counts: 0 8
## probabilities: 0.000 1.000
##
## Node number 108: 129 observations, complexity param=0.004678363
## predicted class=N expected loss=0.496124 P(node) =0.06972973
## class counts: 65 64
## probabilities: 0.504 0.496
## left son=216 (22 obs) right son=217 (107 obs)
## Primary splits:
## sprice.root2 < 15.52398 to the left, improve=3.834272, (0 missing)
##
## Node number 109: 104 observations
## predicted class=Y expected loss=0.4230769 P(node) =0.05621622
## class counts: 44 60
## probabilities: 0.423 0.577
##
## Node number 216: 22 observations
## predicted class=N expected loss=0.2272727 P(node) =0.01189189
## class counts: 17 5
## probabilities: 0.773 0.227
##
## Node number 217: 107 observations
## predicted class=Y expected loss=0.4485981 P(node) =0.05783784
## class counts: 48 59
## probabilities: 0.449 0.551
##
## n= 1850
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 1850 855 N (0.53783784 0.46216216)
## 2) biddable< 0.5 1015 217 N (0.78620690 0.21379310) *
## 3) biddable>=0.5 835 197 Y (0.23592814 0.76407186)
## 6) sprice.root2>=11.83195 293 146 N (0.50170648 0.49829352)
## 12) sprice.root2>=20.33187 38 9 N (0.76315789 0.23684211) *
## 13) sprice.root2< 20.33187 255 118 Y (0.46274510 0.53725490)
## 26) sprice.root2< 12.24684 14 5 N (0.64285714 0.35714286) *
## 27) sprice.root2>=12.24684 241 109 Y (0.45228216 0.54771784)
## 54) sprice.root2>=12.24724 233 109 Y (0.46781116 0.53218884)
## 108) sprice.root2>=14.35185 129 64 N (0.50387597 0.49612403)
## 216) sprice.root2< 15.52398 22 5 N (0.77272727 0.22727273) *
## 217) sprice.root2>=15.52398 107 48 Y (0.44859813 0.55140187) *
## 109) sprice.root2< 14.35185 104 44 Y (0.42307692 0.57692308) *
## 55) sprice.root2< 12.24724 8 0 Y (0.00000000 1.00000000) *
## 7) sprice.root2< 11.83195 542 50 Y (0.09225092 0.90774908) *
## sold.fctr sold.fctr.predict.Max.cor.Y.Train.rpart.N
## 1 N 853
## 2 Y 236
## sold.fctr.predict.Max.cor.Y.Train.rpart.Y
## 1 142
## 2 619
## Prediction
## Reference N Y
## N 853 142
## Y 236 619
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.956757e-01 5.857939e-01 7.765698e-01 8.138392e-01 5.378378e-01
## AccuracyPValue McnemarPValue
## 1.386956e-118 1.723514e-06
## id feats max.nTuningRuns
## 1 Max.cor.Y.Train.rpart biddable,sprice.root2 5
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 1.946 0.015 0.7906315
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.8572864 0.7239766 0.8165347 0.5
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.7660891 0.7783853 0.7765698
## max.AccuracyUpper.fit max.Kappa.fit max.AccuracySD.fit max.KappaSD.fit
## 1 0.8138392 0.5492395 0.01626935 0.0367951## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.3## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.3
## [1] "fitting model: Low.cor.X.Train.glmnet"
## [1] " indep_vars: biddable,spdiff.cut.fctr,startprice.dcm2.is9,D.wrds.stop.n.log,D.weight.sum.stem.stop.Ratio,D.ratio.weight.sum.wrds.n,.rnorm,storage.fctr,D.chrs.pnct11.n.log,startprice.dgt2.is9,color.fctr,prdl.my.fctr,D.terms.post.stop.n.log,cellular.fctr,startprice.dgt1.is9,condition.fctr,sprice.root2,prdl.my.fctr:.clusterid.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.775, lambda = 0.00548 on full training set
## Length Class Mode
## a0 85 -none- numeric
## beta 5270 dgCMatrix S4
## df 85 -none- numeric
## dim 2 -none- numeric
## lambda 85 -none- numeric
## dev.ratio 85 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 62 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## -0.527553658
## D.ratio.weight.sum.wrds.n
## -0.006004383
## D.terms.post.stop.n.log
## -0.036057636
## D.weight.sum.stem.stop.Ratio
## -0.342265842
## biddable
## 1.945468584
## cellular.fctr1
## 0.112733353
## cellular.fctrUnknown
## -0.152915653
## color.fctrWhite
## -0.028006017
## condition.fctrFor parts or not working
## -0.293964206
## condition.fctrNew other (see details)
## 0.481962672
## condition.fctrSeller refurbished
## -0.460245985
## prdl.my.fctriPad1
## -0.546977471
## prdl.my.fctriPad2
## -0.387412751
## prdl.my.fctriPad4
## 0.216740440
## prdl.my.fctriPadAir
## 0.656002229
## prdl.my.fctriPadAir2
## 0.987764705
## prdl.my.fctriPadmini
## -0.197029152
## prdl.my.fctriPadmini2
## 0.219576953
## spdiff.cut.fctr(-100,-10]
## 1.126006752
## spdiff.cut.fctr(-10,-1]
## 2.676037835
## spdiff.cut.fctr(-1,0]
## 3.648460520
## spdiff.cut.fctr(0,1]
## 2.829156143
## spdiff.cut.fctr(1,10]
## 2.461827398
## spdiff.cut.fctr(10,100]
## 2.011679788
## spdiff.cut.fctr(100,1e+03]
## 0.268531135
## sprice.root2
## -0.122692360
## startprice.dcm2.is9
## -0.090103049
## startprice.dgt1.is9
## -0.034868886
## storage.fctr16
## -0.016758016
## storage.fctr32
## -0.015970508
## storage.fctr64
## 0.262200638
## prdl.my.fctrUnknown:.clusterid.fctr2
## 0.434525171
## prdl.my.fctriPad1:.clusterid.fctr2
## -0.412007268
## prdl.my.fctriPad3:.clusterid.fctr2
## -0.251468697
## prdl.my.fctrUnknown:.clusterid.fctr3
## -0.057593533
## prdl.my.fctriPad1:.clusterid.fctr3
## -0.693941981
## prdl.my.fctriPad4:.clusterid.fctr3
## -2.329599807
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## -0.3668865549
## D.chrs.pnct11.n.log
## -0.0061207236
## D.ratio.weight.sum.wrds.n
## -0.0111396841
## D.terms.post.stop.n.log
## -0.0363767333
## D.weight.sum.stem.stop.Ratio
## -0.5243363456
## D.wrds.stop.n.log
## 0.0004472571
## biddable
## 1.9671376842
## cellular.fctr1
## 0.1208206395
## cellular.fctrUnknown
## -0.1863595441
## color.fctrWhite
## -0.0367495691
## condition.fctrFor parts or not working
## -0.3091491053
## condition.fctrNew other (see details)
## 0.5038754128
## condition.fctrSeller refurbished
## -0.4847975090
## prdl.my.fctriPad1
## -0.5754507577
## prdl.my.fctriPad2
## -0.4080986072
## prdl.my.fctriPad4
## 0.2412801685
## prdl.my.fctriPadAir
## 0.6787268845
## prdl.my.fctriPadAir2
## 1.0063500131
## prdl.my.fctriPadmini
## -0.2111939771
## prdl.my.fctriPadmini2
## 0.2339625556
## spdiff.cut.fctr(-100,-10]
## 1.2021007257
## spdiff.cut.fctr(-10,-1]
## 2.7688468072
## spdiff.cut.fctr(-1,0]
## 3.7855102501
## spdiff.cut.fctr(0,1]
## 2.9567435745
## spdiff.cut.fctr(1,10]
## 2.5582379159
## spdiff.cut.fctr(10,100]
## 2.0932467523
## spdiff.cut.fctr(100,1e+03]
## 0.3388178695
## sprice.root2
## -0.1238335982
## startprice.dcm2.is9
## -0.1028307775
## startprice.dgt1.is9
## -0.0341353765
## storage.fctr16
## -0.0576768357
## storage.fctr32
## -0.0552563021
## storage.fctr64
## 0.2392427699
## prdl.my.fctrUnknown:.clusterid.fctr2
## 0.4611515124
## prdl.my.fctriPad1:.clusterid.fctr2
## -0.4305519524
## prdl.my.fctriPad3:.clusterid.fctr2
## -0.2896804907
## prdl.my.fctriPadAir:.clusterid.fctr2
## 0.0075896957
## prdl.my.fctriPadmini2:.clusterid.fctr2
## -0.0402343363
## prdl.my.fctrUnknown:.clusterid.fctr3
## -0.0955633211
## prdl.my.fctriPad1:.clusterid.fctr3
## -0.7247933150
## prdl.my.fctriPad4:.clusterid.fctr3
## -2.4690253355
## sold.fctr sold.fctr.predict.Low.cor.X.Train.glmnet.N
## 1 N 825
## 2 Y 136
## sold.fctr.predict.Low.cor.X.Train.glmnet.Y
## 1 170
## 2 719
## Prediction
## Reference N Y
## N 825 170
## Y 136 719
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.345946e-01 6.682120e-01 8.168663e-01 8.512548e-01 5.378378e-01
## AccuracyPValue McnemarPValue
## 2.686018e-160 5.922970e-02
## id
## 1 Low.cor.X.Train.glmnet
## feats
## 1 biddable,spdiff.cut.fctr,startprice.dcm2.is9,D.wrds.stop.n.log,D.weight.sum.stem.stop.Ratio,D.ratio.weight.sum.wrds.n,.rnorm,storage.fctr,D.chrs.pnct11.n.log,startprice.dgt2.is9,color.fctr,prdl.my.fctr,D.terms.post.stop.n.log,cellular.fctr,startprice.dgt1.is9,condition.fctr,sprice.root2,prdl.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 4.221 0.196
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8316201 0.8994975 0.7637427 0.9082336
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.8245413 0.8227067
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8168663 0.8512548 0.6406244
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01104454 0.0231594## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.3## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.3
## [1] "fitting model: CSM.X.Train.bayesglm"
## [1] " indep_vars: spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr"
## + Fold1.Rep1: parameter=none
## - Fold1.Rep1: parameter=none
## + Fold2.Rep1: parameter=none
## - Fold2.Rep1: parameter=none
## + Fold3.Rep1: parameter=none
## - Fold3.Rep1: parameter=none
## + Fold1.Rep2: parameter=none
## - Fold1.Rep2: parameter=none
## + Fold2.Rep2: parameter=none
## - Fold2.Rep2: parameter=none
## + Fold3.Rep2: parameter=none
## - Fold3.Rep2: parameter=none
## + Fold1.Rep3: parameter=none
## - Fold1.Rep3: parameter=none
## + Fold2.Rep3: parameter=none
## - Fold2.Rep3: parameter=none
## + Fold3.Rep3: parameter=none
## - Fold3.Rep3: parameter=none
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.9411 -0.5900 -0.1676 0.4408 3.1669
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) 6.051053 4.152072 1.457
## .rnorm -0.029503 0.069028 -0.427
## D.chrs.n.log -0.165017 0.432931 -0.381
## D.chrs.pnct11.n.log -0.238267 0.256806 -0.928
## D.chrs.pnct13.n.log -0.357089 0.238989 -1.494
## D.chrs.uppr.n.log -0.182046 0.433304 -0.420
## D.ratio.weight.sum.wrds.n -0.116089 0.211644 -0.549
## D.ratio.wrds.stop.n.wrds.n -1.935728 1.676459 -1.155
## D.terms.post.stem.n.log -0.287677 0.873877 -0.329
## D.terms.post.stop.n.log -1.151597 1.031725 -1.116
## D.weight.post.stem.sum -0.024698 0.369151 -0.067
## D.weight.post.stop.sum -0.004895 0.352961 -0.014
## D.weight.sum -0.024698 0.369151 -0.067
## D.weight.sum.stem.stop.Ratio -3.190008 3.419545 -0.933
## D.wrds.n.log 1.702662 0.920583 1.850
## D.wrds.stop.n.log 0.115999 0.359499 0.323
## D.wrds.unq.n.log -0.287677 0.873877 -0.329
## cellular.fctr1 0.204465 1.281396 0.160
## cellular.fctrUnknown -0.270426 1.636327 -0.165
## color.fctrGold -0.092872 0.450032 -0.206
## `color.fctrSpace Gray` -0.045450 0.277477 -0.164
## color.fctrUnknown -0.074699 0.191556 -0.390
## color.fctrWhite -0.186488 0.210904 -0.884
## `condition.fctrFor parts or not working` -0.626920 0.285857 -2.193
## `condition.fctrManufacturer refurbished` -0.281383 0.474659 -0.593
## condition.fctrNew 0.137776 0.263998 0.522
## `condition.fctrNew other (see details)` 0.682733 0.372003 1.835
## `condition.fctrSeller refurbished` -0.766336 0.319257 -2.400
## prdl.my.fctriPad1 -1.037683 0.398571 -2.604
## prdl.my.fctriPad2 -0.617538 0.348191 -1.774
## prdl.my.fctriPad3 -0.005526 0.385407 -0.014
## prdl.my.fctriPad4 0.602803 0.407515 1.479
## prdl.my.fctriPadAir 0.996871 0.399884 2.493
## prdl.my.fctriPadAir2 1.326366 0.446441 2.971
## prdl.my.fctriPadmini -0.417089 0.342179 -1.219
## prdl.my.fctriPadmini2 0.370358 0.416964 0.888
## prdl.my.fctriPadmini3 0.327475 0.483376 0.677
## `spdiff.cut.fctr(-100,-10]` 1.968077 0.411214 4.786
## `spdiff.cut.fctr(-10,-1]` 3.843580 0.461663 8.326
## `spdiff.cut.fctr(-1,0]` 4.790560 0.760338 6.301
## `spdiff.cut.fctr(0,1]` 4.173558 0.934760 4.465
## `spdiff.cut.fctr(1,10]` 3.375477 0.466238 7.240
## `spdiff.cut.fctr(10,100]` 2.700345 0.434687 6.212
## `spdiff.cut.fctr(100,1e+03]` -0.099366 0.690081 -0.144
## biddable 1.932330 0.423599 4.562
## sprice.d20nexp -0.998266 1.340179 -0.745
## sprice.log10 -0.291394 0.327215 -0.891
## sprice.root2 -0.143183 0.057383 -2.495
## startprice.dcm1.is9 -0.627929 0.257506 -2.439
## startprice.dcm2.is9 0.190525 0.255823 0.745
## startprice.dgt1.is9 0.127608 0.184280 0.692
## startprice.dgt2.is9 0.105489 0.211420 0.499
## storage.fctr16 -0.750936 0.399212 -1.881
## storage.fctr32 -0.729335 0.413837 -1.762
## storage.fctr64 -0.195187 0.397209 -0.491
## storage.fctrUnknown -0.108733 0.513098 -0.212
## `cellular.fctr0:carrier.fctrNone` 0.033702 1.278653 0.026
## `cellular.fctr1:carrier.fctrNone` 0.000000 2.500000 0.000
## `cellular.fctrUnknown:carrier.fctrNone` 0.000000 2.500000 0.000
## `cellular.fctr0:carrier.fctrOther` 0.000000 2.500000 0.000
## `cellular.fctr1:carrier.fctrOther` 0.417364 1.907301 0.219
## `cellular.fctrUnknown:carrier.fctrOther` 0.000000 2.500000 0.000
## `cellular.fctr0:carrier.fctrSprint` 0.000000 2.500000 0.000
## `cellular.fctr1:carrier.fctrSprint` 0.506115 0.532074 0.951
## `cellular.fctrUnknown:carrier.fctrSprint` 0.000000 2.500000 0.000
## `cellular.fctr0:carrier.fctrT-Mobile` 0.000000 2.500000 0.000
## `cellular.fctr1:carrier.fctrT-Mobile` 0.296578 0.635277 0.467
## `cellular.fctrUnknown:carrier.fctrT-Mobile` 0.000000 2.500000 0.000
## `cellular.fctr0:carrier.fctrUnknown` 0.000000 2.500000 0.000
## `cellular.fctr1:carrier.fctrUnknown` -0.090769 0.385216 -0.236
## `cellular.fctrUnknown:carrier.fctrUnknown` -0.270426 1.636327 -0.165
## `cellular.fctr0:carrier.fctrVerizon` 0.000000 2.500000 0.000
## `cellular.fctr1:carrier.fctrVerizon` 0.096646 0.323319 0.299
## `cellular.fctrUnknown:carrier.fctrVerizon` 0.000000 2.500000 0.000
## `prdl.my.fctrUnknown:.clusterid.fctr2` 0.919932 0.588404 1.563
## `prdl.my.fctriPad1:.clusterid.fctr2` -0.601143 0.547905 -1.097
## `prdl.my.fctriPad2:.clusterid.fctr2` 0.000000 2.500000 0.000
## `prdl.my.fctriPad3:.clusterid.fctr2` -0.616843 0.671063 -0.919
## `prdl.my.fctriPad4:.clusterid.fctr2` -0.110709 0.706327 -0.157
## `prdl.my.fctriPadAir:.clusterid.fctr2` 0.376800 0.555602 0.678
## `prdl.my.fctriPadAir2:.clusterid.fctr2` 0.000000 2.500000 0.000
## `prdl.my.fctriPadmini:.clusterid.fctr2` 0.000000 2.500000 0.000
## `prdl.my.fctriPadmini2:.clusterid.fctr2` -0.354722 0.783439 -0.453
## `prdl.my.fctriPadmini3:.clusterid.fctr2` 0.000000 2.500000 0.000
## `prdl.my.fctrUnknown:.clusterid.fctr3` -0.091922 0.747094 -0.123
## `prdl.my.fctriPad1:.clusterid.fctr3` -1.257529 0.659213 -1.908
## `prdl.my.fctriPad2:.clusterid.fctr3` 0.000000 2.500000 0.000
## `prdl.my.fctriPad3:.clusterid.fctr3` 0.000000 2.500000 0.000
## `prdl.my.fctriPad4:.clusterid.fctr3` -4.377683 1.698080 -2.578
## `prdl.my.fctriPadAir:.clusterid.fctr3` 0.000000 2.500000 0.000
## `prdl.my.fctriPadAir2:.clusterid.fctr3` 0.000000 2.500000 0.000
## `prdl.my.fctriPadmini:.clusterid.fctr3` 0.000000 2.500000 0.000
## `prdl.my.fctriPadmini2:.clusterid.fctr3` 0.000000 2.500000 0.000
## `prdl.my.fctriPadmini3:.clusterid.fctr3` 0.000000 2.500000 0.000
## `spdiff.cut.fctr(-100,-10]:biddable` 0.025009 0.467332 0.054
## `spdiff.cut.fctr(-10,-1]:biddable` -0.648096 0.582993 -1.112
## `spdiff.cut.fctr(-1,0]:biddable` 1.509599 1.602456 0.942
## `spdiff.cut.fctr(0,1]:biddable` 0.165615 1.191063 0.139
## `spdiff.cut.fctr(1,10]:biddable` 0.154565 0.617026 0.250
## `spdiff.cut.fctr(10,100]:biddable` 0.389698 0.532086 0.732
## `spdiff.cut.fctr(100,1e+03]:biddable` 2.818527 1.067823 2.640
## Pr(>|z|)
## (Intercept) 0.14502
## .rnorm 0.66909
## D.chrs.n.log 0.70308
## D.chrs.pnct11.n.log 0.35351
## D.chrs.pnct13.n.log 0.13513
## D.chrs.uppr.n.log 0.67439
## D.ratio.weight.sum.wrds.n 0.58334
## D.ratio.wrds.stop.n.wrds.n 0.24823
## D.terms.post.stem.n.log 0.74201
## D.terms.post.stop.n.log 0.26434
## D.weight.post.stem.sum 0.94666
## D.weight.post.stop.sum 0.98894
## D.weight.sum 0.94666
## D.weight.sum.stem.stop.Ratio 0.35088
## D.wrds.n.log 0.06438 .
## D.wrds.stop.n.log 0.74695
## D.wrds.unq.n.log 0.74201
## cellular.fctr1 0.87322
## cellular.fctrUnknown 0.86874
## color.fctrGold 0.83650
## `color.fctrSpace Gray` 0.86989
## color.fctrUnknown 0.69657
## color.fctrWhite 0.37657
## `condition.fctrFor parts or not working` 0.02830 *
## `condition.fctrManufacturer refurbished` 0.55331
## condition.fctrNew 0.60175
## `condition.fctrNew other (see details)` 0.06646 .
## `condition.fctrSeller refurbished` 0.01638 *
## prdl.my.fctriPad1 0.00923 **
## prdl.my.fctriPad2 0.07614 .
## prdl.my.fctriPad3 0.98856
## prdl.my.fctriPad4 0.13908
## prdl.my.fctriPadAir 0.01267 *
## prdl.my.fctriPadAir2 0.00297 **
## prdl.my.fctriPadmini 0.22287
## prdl.my.fctriPadmini2 0.37442
## prdl.my.fctriPadmini3 0.49810
## `spdiff.cut.fctr(-100,-10]` 1.70e-06 ***
## `spdiff.cut.fctr(-10,-1]` < 2e-16 ***
## `spdiff.cut.fctr(-1,0]` 2.97e-10 ***
## `spdiff.cut.fctr(0,1]` 8.01e-06 ***
## `spdiff.cut.fctr(1,10]` 4.49e-13 ***
## `spdiff.cut.fctr(10,100]` 5.23e-10 ***
## `spdiff.cut.fctr(100,1e+03]` 0.88551
## biddable 5.07e-06 ***
## sprice.d20nexp 0.45635
## sprice.log10 0.37318
## sprice.root2 0.01259 *
## startprice.dcm1.is9 0.01475 *
## startprice.dcm2.is9 0.45642
## startprice.dgt1.is9 0.48865
## startprice.dgt2.is9 0.61781
## storage.fctr16 0.05997 .
## storage.fctr32 0.07801 .
## storage.fctr64 0.62315
## storage.fctrUnknown 0.83217
## `cellular.fctr0:carrier.fctrNone` 0.97897
## `cellular.fctr1:carrier.fctrNone` 1.00000
## `cellular.fctrUnknown:carrier.fctrNone` 1.00000
## `cellular.fctr0:carrier.fctrOther` 1.00000
## `cellular.fctr1:carrier.fctrOther` 0.82679
## `cellular.fctrUnknown:carrier.fctrOther` 1.00000
## `cellular.fctr0:carrier.fctrSprint` 1.00000
## `cellular.fctr1:carrier.fctrSprint` 0.34150
## `cellular.fctrUnknown:carrier.fctrSprint` 1.00000
## `cellular.fctr0:carrier.fctrT-Mobile` 1.00000
## `cellular.fctr1:carrier.fctrT-Mobile` 0.64061
## `cellular.fctrUnknown:carrier.fctrT-Mobile` 1.00000
## `cellular.fctr0:carrier.fctrUnknown` 1.00000
## `cellular.fctr1:carrier.fctrUnknown` 0.81372
## `cellular.fctrUnknown:carrier.fctrUnknown` 0.86874
## `cellular.fctr0:carrier.fctrVerizon` 1.00000
## `cellular.fctr1:carrier.fctrVerizon` 0.76500
## `cellular.fctrUnknown:carrier.fctrVerizon` 1.00000
## `prdl.my.fctrUnknown:.clusterid.fctr2` 0.11795
## `prdl.my.fctriPad1:.clusterid.fctr2` 0.27257
## `prdl.my.fctriPad2:.clusterid.fctr2` 1.00000
## `prdl.my.fctriPad3:.clusterid.fctr2` 0.35799
## `prdl.my.fctriPad4:.clusterid.fctr2` 0.87545
## `prdl.my.fctriPadAir:.clusterid.fctr2` 0.49766
## `prdl.my.fctriPadAir2:.clusterid.fctr2` 1.00000
## `prdl.my.fctriPadmini:.clusterid.fctr2` 1.00000
## `prdl.my.fctriPadmini2:.clusterid.fctr2` 0.65071
## `prdl.my.fctriPadmini3:.clusterid.fctr2` 1.00000
## `prdl.my.fctrUnknown:.clusterid.fctr3` 0.90208
## `prdl.my.fctriPad1:.clusterid.fctr3` 0.05644 .
## `prdl.my.fctriPad2:.clusterid.fctr3` 1.00000
## `prdl.my.fctriPad3:.clusterid.fctr3` 1.00000
## `prdl.my.fctriPad4:.clusterid.fctr3` 0.00994 **
## `prdl.my.fctriPadAir:.clusterid.fctr3` 1.00000
## `prdl.my.fctriPadAir2:.clusterid.fctr3` 1.00000
## `prdl.my.fctriPadmini:.clusterid.fctr3` 1.00000
## `prdl.my.fctriPadmini2:.clusterid.fctr3` 1.00000
## `prdl.my.fctriPadmini3:.clusterid.fctr3` 1.00000
## `spdiff.cut.fctr(-100,-10]:biddable` 0.95732
## `spdiff.cut.fctr(-10,-1]:biddable` 0.26628
## `spdiff.cut.fctr(-1,0]:biddable` 0.34617
## `spdiff.cut.fctr(0,1]:biddable` 0.88941
## `spdiff.cut.fctr(1,10]:biddable` 0.80220
## `spdiff.cut.fctr(10,100]:biddable` 0.46393
## `spdiff.cut.fctr(100,1e+03]:biddable` 0.00830 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2554.0 on 1849 degrees of freedom
## Residual deviance: 1327.6 on 1749 degrees of freedom
## AIC: 1529.6
##
## Number of Fisher Scoring iterations: 40
## sold.fctr sold.fctr.predict.CSM.X.Train.bayesglm.N
## 1 N 834
## 2 Y 138
## sold.fctr.predict.CSM.X.Train.bayesglm.Y
## 1 161
## 2 717
## Prediction
## Reference N Y
## N 834 161
## Y 138 717
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.383784e-01 6.755090e-01 8.208005e-01 8.548759e-01 5.378378e-01
## AccuracyPValue McnemarPValue
## 8.476544e-165 2.032691e-01
## id
## 1 CSM.X.Train.bayesglm
## feats
## 1 spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,.rnorm,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 8.667 1.198
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8366188 0.8884422 0.7847953 0.9179559
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.8274668 0.821083
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8208005 0.8548759 0.6379
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.009985233 0.02105923## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.3## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.3
## [1] "fitting model: CSM.X.Train.earth"
## [1] " indep_vars: spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting nprune = 26, degree = 1 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdlIdPfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: nprune## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdlIdPfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: degree
## Call: earth(x=matrix[1850,99], y=factor.object, keepxy=TRUE,
## glm=list(family=function.object), degree=1, nprune=26)
##
## GLM coefficients
## Y
## (Intercept) -16.3145386
## prdl.my.fctriPad1 -0.7471562
## prdl.my.fctriPadAir 1.0477655
## prdl.my.fctriPadAir2 1.1539149
## spdiff.cut.fctr(-100,-10] 2.0152376
## spdiff.cut.fctr(-10,-1] 3.9059908
## spdiff.cut.fctr(-1,0] 5.5174587
## spdiff.cut.fctr(0,1] 4.5053493
## spdiff.cut.fctr(1,10] 3.5008812
## spdiff.cut.fctr(10,100] 2.9231472
## biddable 2.2285619
## startprice.dcm1.is9 -0.4316652
## prdl.my.fctriPad4:.clusterid.fctr3 -15.3347618
## spdiff.cut.fctr(-100,-10]:biddable -0.1260536
## spdiff.cut.fctr(-10,-1]:biddable -0.6354337
## spdiff.cut.fctr(1,10]:biddable 0.1828529
## spdiff.cut.fctr(10,100]:biddable 0.4427776
## spdiff.cut.fctr(100,1e+03]:biddable 3.6110187
## h(2.07944-D.terms.post.stem.n.log) -17.2557730
## h(D.terms.post.stem.n.log-2.07944) 16.4855696
## h(2.89037-D.terms.post.stop.n.log) 17.1746676
## h(0.949004-D.weight.sum.stem.stop.Ratio) 7.8167439
## h(sprice.log10-3.80644) -0.7248261
##
## Earth selected 23 of 33 terms, and 21 of 99 predictors
## Termination condition: RSq changed by less than 0.001 at 33 terms
## Importance: biddable, spdiff.cut.fctr(-10,-1], ...
## Number of terms at each degree of interaction: 1 22 (additive model)
## Earth GCV 0.1265335 RSS 222.8375 GRSq 0.4915008 RSq 0.515414
##
## GLM null.deviance 2554.04 (1849 dof) deviance 1380.458 (1827 dof) iters 15
## sold.fctr sold.fctr.predict.CSM.X.Train.earth.N
## 1 N 833
## 2 Y 146
## sold.fctr.predict.CSM.X.Train.earth.Y
## 1 162
## 2 709
## Prediction
## Reference N Y
## N 833 162
## Y 146 709
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.335135e-01 6.655495e-01 8.157428e-01 8.502197e-01 5.378378e-01
## AccuracyPValue McnemarPValue
## 5.008848e-159 3.927149e-01
## id
## 1 CSM.X.Train.earth
## feats
## 1 spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 5 11.15 1.257
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8344353 0.879397 0.7894737 0.9093397
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.8215527 0.8223456
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8157428 0.8502197 0.6406899
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.009068152 0.01949697## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.4## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.4
## [1] "fitting model: Interact.High.cor.Y.Train.glmnet"
## [1] " indep_vars: biddable,sprice.root2,biddable:sprice.log10,biddable:D.terms.post.stop.n.log,biddable:startprice.dcm2.is9,biddable:D.weight.post.stem.sum,biddable:D.chrs.n.log,biddable:cellular.fctr,biddable:D.terms.post.stem.n.log,biddable:sprice.root2"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.00118 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdlIdPfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha
## Length Class Mode
## a0 73 -none- numeric
## beta 803 dgCMatrix S4
## df 73 -none- numeric
## dim 2 -none- numeric
## lambda 73 -none- numeric
## dev.ratio 73 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 11 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) biddable
## 0.020934593 4.142550047
## sprice.root2 biddable:D.terms.post.stem.n.log
## -0.085176133 -0.084992650
## biddable:D.weight.post.stem.sum biddable:cellular.fctr1
## -0.008311932 0.154452614
## biddable:cellular.fctrUnknown biddable:sprice.log10
## -0.959289758 -0.237070702
## biddable:sprice.root2 biddable:startprice.dcm2.is9
## -0.076211012 0.019523725
## [1] "max lambda < lambdaOpt:"
## (Intercept) biddable
## 0.014568162 4.193429342
## sprice.root2 biddable:D.terms.post.stem.n.log
## -0.084789869 -0.085216004
## biddable:D.weight.post.stem.sum biddable:cellular.fctr1
## -0.009279166 0.155978580
## biddable:cellular.fctrUnknown biddable:sprice.log10
## -0.966574102 -0.249650060
## biddable:sprice.root2 biddable:startprice.dcm2.is9
## -0.075008805 0.016910054
## sold.fctr sold.fctr.predict.Interact.High.cor.Y.Train.glmnet.N
## 1 N 830
## 2 Y 233
## sold.fctr.predict.Interact.High.cor.Y.Train.glmnet.Y
## 1 165
## 2 622
## Prediction
## Reference N Y
## N 830 165
## Y 233 622
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.848649e-01 5.648165e-01 7.654256e-01 8.033966e-01 5.378378e-01
## AccuracyPValue McnemarPValue
## 2.251720e-108 7.839323e-04
## id
## 1 Interact.High.cor.Y.Train.glmnet
## feats
## 1 biddable,sprice.root2,biddable:sprice.log10,biddable:D.terms.post.stop.n.log,biddable:startprice.dcm2.is9,biddable:D.weight.post.stem.sum,biddable:D.chrs.n.log,biddable:cellular.fctr,biddable:D.terms.post.stem.n.log,biddable:sprice.root2
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 3.62 0.099
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.7812013 0.8653266 0.697076 0.8454154
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.7576127 0.7872117
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.7654256 0.8033966 0.567169
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.0131625 0.02814585## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.3## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.3
## [1] "fitting model: CSM.X.Train.rpart"
## [1] " indep_vars: spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr"
## + Fold1.Rep1: cp=0.007018
## - Fold1.Rep1: cp=0.007018
## + Fold2.Rep1: cp=0.007018
## - Fold2.Rep1: cp=0.007018
## + Fold3.Rep1: cp=0.007018
## - Fold3.Rep1: cp=0.007018
## + Fold1.Rep2: cp=0.007018
## - Fold1.Rep2: cp=0.007018
## + Fold2.Rep2: cp=0.007018
## - Fold2.Rep2: cp=0.007018
## + Fold3.Rep2: cp=0.007018
## - Fold3.Rep2: cp=0.007018
## + Fold1.Rep3: cp=0.007018
## - Fold1.Rep3: cp=0.007018
## + Fold2.Rep3: cp=0.007018
## - Fold2.Rep3: cp=0.007018
## + Fold3.Rep3: cp=0.007018
## - Fold3.Rep3: cp=0.007018
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.0129 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 1850
##
## CP nsplit rel error
## 1 0.51578947 0 1.0000000
## 2 0.02397661 1 0.4842105
## 3 0.01871345 3 0.4362573
## 4 0.01286550 4 0.4175439
##
## Variable importance
## biddable sprice.d20nexp
## 27 18
## sprice.log10 sprice.root2
## 18 18
## spdiff.cut.fctr(-100,-10]:biddable spdiff.cut.fctr(10,100]:biddable
## 7 7
## spdiff.cut.fctr(10,100] condition.fctrNew
## 2 1
## spdiff.cut.fctr(-100,-10] prdl.my.fctriPadAir2
## 1 1
##
## Node number 1: 1850 observations, complexity param=0.5157895
## predicted class=N expected loss=0.4621622 P(node) =1
## class counts: 995 855
## probabilities: 0.538 0.462
## left son=2 (1015 obs) right son=3 (835 obs)
## Primary splits:
## biddable < 0.5 to the left, improve=277.4446, (0 missing)
## sprice.d20nexp < 0.00657364 to the left, improve=217.5035, (0 missing)
## sprice.log10 < 4.610145 to the right, improve=217.5035, (0 missing)
## sprice.root2 < 10.02494 to the right, improve=217.5035, (0 missing)
## spdiff.cut.fctr(-1,0] < 0.5 to the left, improve= 72.7748, (0 missing)
## Surrogate splits:
## sprice.d20nexp < 0.00657364 to the left, agree=0.750, adj=0.447, (0 split)
## sprice.log10 < 4.610145 to the right, agree=0.750, adj=0.447, (0 split)
## sprice.root2 < 10.02494 to the right, agree=0.750, adj=0.447, (0 split)
## spdiff.cut.fctr(-100,-10]:biddable < 0.5 to the left, agree=0.650, adj=0.225, (0 split)
## spdiff.cut.fctr(10,100]:biddable < 0.5 to the left, agree=0.626, adj=0.172, (0 split)
##
## Node number 2: 1015 observations
## predicted class=N expected loss=0.2137931 P(node) =0.5486486
## class counts: 798 217
## probabilities: 0.786 0.214
##
## Node number 3: 835 observations, complexity param=0.02397661
## predicted class=Y expected loss=0.2359281 P(node) =0.4513514
## class counts: 197 638
## probabilities: 0.236 0.764
## left son=6 (293 obs) right son=7 (542 obs)
## Primary splits:
## sprice.d20nexp < 0.00091211 to the left, improve=63.77111, (0 missing)
## sprice.log10 < 4.941607 to the right, improve=63.77111, (0 missing)
## sprice.root2 < 11.83195 to the right, improve=63.77111, (0 missing)
## spdiff.cut.fctr(-1,0] < 0.5 to the left, improve=14.84709, (0 missing)
## spdiff.cut.fctr(-1,0]:biddable < 0.5 to the left, improve=14.84709, (0 missing)
## Surrogate splits:
## sprice.log10 < 4.941607 to the right, agree=1.000, adj=1.000, (0 split)
## sprice.root2 < 11.83195 to the right, agree=1.000, adj=1.000, (0 split)
## condition.fctrNew < 0.5 to the right, agree=0.698, adj=0.140, (0 split)
## prdl.my.fctriPadAir2 < 0.5 to the right, agree=0.683, adj=0.096, (0 split)
## prdl.my.fctriPadAir < 0.5 to the right, agree=0.666, adj=0.048, (0 split)
##
## Node number 6: 293 observations, complexity param=0.02397661
## predicted class=N expected loss=0.4982935 P(node) =0.1583784
## class counts: 147 146
## probabilities: 0.502 0.498
## left son=12 (247 obs) right son=13 (46 obs)
## Primary splits:
## spdiff.cut.fctr(10,100] < 0.5 to the left, improve=20.792430, (0 missing)
## spdiff.cut.fctr(10,100]:biddable < 0.5 to the left, improve=20.792430, (0 missing)
## sprice.d20nexp < 1.297877e-09 to the left, improve= 5.969295, (0 missing)
## sprice.log10 < 6.024113 to the right, improve= 5.969295, (0 missing)
## sprice.root2 < 20.33187 to the right, improve= 5.969295, (0 missing)
## Surrogate splits:
## spdiff.cut.fctr(10,100]:biddable < 0.5 to the left, agree=1, adj=1, (0 split)
##
## Node number 7: 542 observations
## predicted class=Y expected loss=0.09225092 P(node) =0.292973
## class counts: 50 492
## probabilities: 0.092 0.908
##
## Node number 12: 247 observations, complexity param=0.01871345
## predicted class=N expected loss=0.417004 P(node) =0.1335135
## class counts: 144 103
## probabilities: 0.583 0.417
## left son=24 (161 obs) right son=25 (86 obs)
## Primary splits:
## spdiff.cut.fctr(-100,-10] < 0.5 to the left, improve=8.175600, (0 missing)
## spdiff.cut.fctr(-100,-10]:biddable < 0.5 to the left, improve=8.175600, (0 missing)
## spdiff.cut.fctr(100,1e+03] < 0.5 to the left, improve=7.825980, (0 missing)
## spdiff.cut.fctr(100,1e+03]:biddable < 0.5 to the left, improve=7.825980, (0 missing)
## sprice.log10 < 6.024113 to the right, improve=4.562231, (0 missing)
## Surrogate splits:
## spdiff.cut.fctr(-100,-10]:biddable < 0.5 to the left, agree=1.000, adj=1.000, (0 split)
## sprice.d20nexp < 0.0001586596 to the left, agree=0.696, adj=0.128, (0 split)
## sprice.log10 < 5.075143 to the right, agree=0.696, adj=0.128, (0 split)
## sprice.root2 < 12.64891 to the right, agree=0.696, adj=0.128, (0 split)
## prdl.my.fctriPad2 < 0.5 to the left, agree=0.668, adj=0.047, (0 split)
##
## Node number 13: 46 observations
## predicted class=Y expected loss=0.06521739 P(node) =0.02486486
## class counts: 3 43
## probabilities: 0.065 0.935
##
## Node number 24: 161 observations
## predicted class=N expected loss=0.3229814 P(node) =0.08702703
## class counts: 109 52
## probabilities: 0.677 0.323
##
## Node number 25: 86 observations
## predicted class=Y expected loss=0.4069767 P(node) =0.04648649
## class counts: 35 51
## probabilities: 0.407 0.593
##
## n= 1850
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 1850 855 N (0.53783784 0.46216216)
## 2) biddable< 0.5 1015 217 N (0.78620690 0.21379310) *
## 3) biddable>=0.5 835 197 Y (0.23592814 0.76407186)
## 6) sprice.d20nexp< 0.00091211 293 146 N (0.50170648 0.49829352)
## 12) spdiff.cut.fctr(10,100]< 0.5 247 103 N (0.58299595 0.41700405)
## 24) spdiff.cut.fctr(-100,-10]< 0.5 161 52 N (0.67701863 0.32298137) *
## 25) spdiff.cut.fctr(-100,-10]>=0.5 86 35 Y (0.40697674 0.59302326) *
## 13) spdiff.cut.fctr(10,100]>=0.5 46 3 Y (0.06521739 0.93478261) *
## 7) sprice.d20nexp>=0.00091211 542 50 Y (0.09225092 0.90774908) *
## sold.fctr sold.fctr.predict.CSM.X.Train.rpart.N
## 1 N 907
## 2 Y 269
## sold.fctr.predict.CSM.X.Train.rpart.Y
## 1 88
## 2 586
## Prediction
## Reference N Y
## N 907 88
## Y 269 586
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.070270e-01 6.059626e-01 7.882930e-01 8.247822e-01 5.378378e-01
## AccuracyPValue McnemarPValue
## 6.312953e-130 1.625028e-21
## id
## 1 CSM.X.Train.rpart
## feats
## 1 spdiff.cut.fctr*biddable,sprice.d20nexp,sprice.log10,sprice.root2,cellular.fctr,prdl.my.fctr,D.ratio.wrds.stop.n.wrds.n,storage.fctr,condition.fctr,D.weight.sum.stem.stop.Ratio,color.fctr,startprice.dcm2.is9,D.chrs.pnct11.n.log,D.wrds.stop.n.log,startprice.dcm1.is9,startprice.dgt2.is9,D.ratio.weight.sum.wrds.n,D.chrs.pnct13.n.log,D.weight.post.stop.sum,D.weight.post.stem.sum,D.weight.sum,D.wrds.n.log,D.chrs.uppr.n.log,D.chrs.n.log,D.wrds.unq.n.log,D.terms.post.stem.n.log,D.terms.post.stop.n.log,startprice.dgt1.is9,cellular.fctr:carrier.fctr,prdl.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 5 4.398 0.123
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.798469 0.9115578 0.6853801 0.8187687
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.7665141 0.8030677
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.788293 0.8247822 0.5970603
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01458969 0.03188904## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.3## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id =
## tail(glb_models_df$id, : Using default probability threshold: 0.3## [1] "fitting model: Final.Ensemble.glm"
## [1] " indep_vars: sold.fctr.predict.CSM.X.Train.gbm.prob,sold.fctr.predict.CSM.X.Train.avNNet.prob,sold.fctr.predict.CSM.X.Interact.Train.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.Train.rpart.prob,sold.fctr.predict.CSM.X.center.scale.Train.bagEarth.prob,sold.fctr.predict.CSM.X.Train.bagEarth.prob,sold.fctr.predict.CSM.X.YeoJohnson.Train.bagEarth.prob,sold.fctr.predict.CSM.X.Train.nnet.prob,sold.fctr.predict.CSM.X.Train.glm.prob,sold.fctr.predict.All.X.Train.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.1X1.Train.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X5.Train.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X1.Train.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X3.Train.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.3X5.Train.glmnet.prob,sold.fctr.predict.RFE.X.Train.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.3X3.Train.glmnet.prob,sold.fctr.predict.CSM.X.spatialSign.Train.bagEarth.prob,sold.fctr.predict.Max.cor.Y.rcv.3X1.Train.glmnet.prob,sold.fctr.predict.RFE.X.Train.glm.prob,sold.fctr.predict.Max.cor.Y.Train.rpart.prob,sold.fctr.predict.Low.cor.X.Train.glmnet.prob,sold.fctr.predict.CSM.X.Train.bayesglm.prob,sold.fctr.predict.CSM.X.Train.earth.prob,sold.fctr.predict.Interact.High.cor.Y.Train.glmnet.prob,sold.fctr.predict.CSM.X.Train.rpart.prob"
## + Fold1.Rep1: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold1.Rep1: parameter=none
## + Fold2.Rep1: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold2.Rep1: parameter=none
## + Fold3.Rep1: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold3.Rep1: parameter=none
## + Fold1.Rep2: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold1.Rep2: parameter=none
## + Fold2.Rep2: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold2.Rep2: parameter=none
## + Fold3.Rep2: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold3.Rep2: parameter=none
## + Fold1.Rep3: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold1.Rep3: parameter=none
## + Fold2.Rep3: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold2.Rep3: parameter=none
## + Fold3.Rep3: parameter=none## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold3.Rep3: parameter=none
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.9862 -0.3927 -0.1908 0.2857 2.9348
##
## Coefficients: (11 not defined because of singularities)
## Estimate
## (Intercept) -3.7582
## sold.fctr.predict.All.X.Train.glmnet.prob -6.9004
## sold.fctr.predict.CSM.X.Interact.Train.glmnet.prob 7.5889
## sold.fctr.predict.CSM.X.Train.avNNet.prob 1.5092
## sold.fctr.predict.CSM.X.Train.bagEarth.prob 6.6742
## sold.fctr.predict.CSM.X.Train.bayesglm.prob -4.2817
## sold.fctr.predict.CSM.X.Train.earth.prob -3.9056
## sold.fctr.predict.CSM.X.Train.gbm.prob 8.4611
## sold.fctr.predict.CSM.X.Train.glm.prob 5.3128
## sold.fctr.predict.CSM.X.Train.nnet.prob 0.2575
## sold.fctr.predict.CSM.X.Train.rpart.prob -3.0751
## sold.fctr.predict.CSM.X.YeoJohnson.Train.bagEarth.prob NA
## sold.fctr.predict.CSM.X.center.scale.Train.bagEarth.prob NA
## sold.fctr.predict.CSM.X.spatialSign.Train.bagEarth.prob NA
## sold.fctr.predict.Interact.High.cor.Y.Train.glmnet.prob -2.9982
## sold.fctr.predict.Low.cor.X.Train.glmnet.prob -4.1685
## sold.fctr.predict.Max.cor.Y.Train.rpart.prob 2.8545
## sold.fctr.predict.Max.cor.Y.rcv.1X1.Train.glmnet.prob 1.4019
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.Train.rpart.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.3X1.Train.glmnet.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.3X3.Train.glmnet.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.3X5.Train.glmnet.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.5X1.Train.glmnet.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.5X3.Train.glmnet.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.5X5.Train.glmnet.prob NA
## sold.fctr.predict.RFE.X.Train.glm.prob -0.8757
## sold.fctr.predict.RFE.X.Train.glmnet.prob NA
## Std. Error
## (Intercept) 0.5465
## sold.fctr.predict.All.X.Train.glmnet.prob 4.8143
## sold.fctr.predict.CSM.X.Interact.Train.glmnet.prob 1.2733
## sold.fctr.predict.CSM.X.Train.avNNet.prob 1.6031
## sold.fctr.predict.CSM.X.Train.bagEarth.prob 1.2558
## sold.fctr.predict.CSM.X.Train.bayesglm.prob 3.5735
## sold.fctr.predict.CSM.X.Train.earth.prob 1.2092
## sold.fctr.predict.CSM.X.Train.gbm.prob 1.1206
## sold.fctr.predict.CSM.X.Train.glm.prob 3.9205
## sold.fctr.predict.CSM.X.Train.nnet.prob 3.6167
## sold.fctr.predict.CSM.X.Train.rpart.prob 0.9640
## sold.fctr.predict.CSM.X.YeoJohnson.Train.bagEarth.prob NA
## sold.fctr.predict.CSM.X.center.scale.Train.bagEarth.prob NA
## sold.fctr.predict.CSM.X.spatialSign.Train.bagEarth.prob NA
## sold.fctr.predict.Interact.High.cor.Y.Train.glmnet.prob 1.4322
## sold.fctr.predict.Low.cor.X.Train.glmnet.prob 4.2417
## sold.fctr.predict.Max.cor.Y.Train.rpart.prob 1.0125
## sold.fctr.predict.Max.cor.Y.rcv.1X1.Train.glmnet.prob 2.5031
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.Train.rpart.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.3X1.Train.glmnet.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.3X3.Train.glmnet.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.3X5.Train.glmnet.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.5X1.Train.glmnet.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.5X3.Train.glmnet.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.5X5.Train.glmnet.prob NA
## sold.fctr.predict.RFE.X.Train.glm.prob 2.9277
## sold.fctr.predict.RFE.X.Train.glmnet.prob NA
## z value Pr(>|z|)
## (Intercept) -6.876 6.15e-12
## sold.fctr.predict.All.X.Train.glmnet.prob -1.433 0.15177
## sold.fctr.predict.CSM.X.Interact.Train.glmnet.prob 5.960 2.52e-09
## sold.fctr.predict.CSM.X.Train.avNNet.prob 0.941 0.34651
## sold.fctr.predict.CSM.X.Train.bagEarth.prob 5.315 1.07e-07
## sold.fctr.predict.CSM.X.Train.bayesglm.prob -1.198 0.23084
## sold.fctr.predict.CSM.X.Train.earth.prob -3.230 0.00124
## sold.fctr.predict.CSM.X.Train.gbm.prob 7.551 4.33e-14
## sold.fctr.predict.CSM.X.Train.glm.prob 1.355 0.17537
## sold.fctr.predict.CSM.X.Train.nnet.prob 0.071 0.94324
## sold.fctr.predict.CSM.X.Train.rpart.prob -3.190 0.00142
## sold.fctr.predict.CSM.X.YeoJohnson.Train.bagEarth.prob NA NA
## sold.fctr.predict.CSM.X.center.scale.Train.bagEarth.prob NA NA
## sold.fctr.predict.CSM.X.spatialSign.Train.bagEarth.prob NA NA
## sold.fctr.predict.Interact.High.cor.Y.Train.glmnet.prob -2.093 0.03631
## sold.fctr.predict.Low.cor.X.Train.glmnet.prob -0.983 0.32573
## sold.fctr.predict.Max.cor.Y.Train.rpart.prob 2.819 0.00481
## sold.fctr.predict.Max.cor.Y.rcv.1X1.Train.glmnet.prob 0.560 0.57543
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.Train.rpart.prob NA NA
## sold.fctr.predict.Max.cor.Y.rcv.3X1.Train.glmnet.prob NA NA
## sold.fctr.predict.Max.cor.Y.rcv.3X3.Train.glmnet.prob NA NA
## sold.fctr.predict.Max.cor.Y.rcv.3X5.Train.glmnet.prob NA NA
## sold.fctr.predict.Max.cor.Y.rcv.5X1.Train.glmnet.prob NA NA
## sold.fctr.predict.Max.cor.Y.rcv.5X3.Train.glmnet.prob NA NA
## sold.fctr.predict.Max.cor.Y.rcv.5X5.Train.glmnet.prob NA NA
## sold.fctr.predict.RFE.X.Train.glm.prob -0.299 0.76485
## sold.fctr.predict.RFE.X.Train.glmnet.prob NA NA
##
## (Intercept) ***
## sold.fctr.predict.All.X.Train.glmnet.prob
## sold.fctr.predict.CSM.X.Interact.Train.glmnet.prob ***
## sold.fctr.predict.CSM.X.Train.avNNet.prob
## sold.fctr.predict.CSM.X.Train.bagEarth.prob ***
## sold.fctr.predict.CSM.X.Train.bayesglm.prob
## sold.fctr.predict.CSM.X.Train.earth.prob **
## sold.fctr.predict.CSM.X.Train.gbm.prob ***
## sold.fctr.predict.CSM.X.Train.glm.prob
## sold.fctr.predict.CSM.X.Train.nnet.prob
## sold.fctr.predict.CSM.X.Train.rpart.prob **
## sold.fctr.predict.CSM.X.YeoJohnson.Train.bagEarth.prob
## sold.fctr.predict.CSM.X.center.scale.Train.bagEarth.prob
## sold.fctr.predict.CSM.X.spatialSign.Train.bagEarth.prob
## sold.fctr.predict.Interact.High.cor.Y.Train.glmnet.prob *
## sold.fctr.predict.Low.cor.X.Train.glmnet.prob
## sold.fctr.predict.Max.cor.Y.Train.rpart.prob **
## sold.fctr.predict.Max.cor.Y.rcv.1X1.Train.glmnet.prob
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.Train.rpart.prob
## sold.fctr.predict.Max.cor.Y.rcv.3X1.Train.glmnet.prob
## sold.fctr.predict.Max.cor.Y.rcv.3X3.Train.glmnet.prob
## sold.fctr.predict.Max.cor.Y.rcv.3X5.Train.glmnet.prob
## sold.fctr.predict.Max.cor.Y.rcv.5X1.Train.glmnet.prob
## sold.fctr.predict.Max.cor.Y.rcv.5X3.Train.glmnet.prob
## sold.fctr.predict.Max.cor.Y.rcv.5X5.Train.glmnet.prob
## sold.fctr.predict.RFE.X.Train.glm.prob
## sold.fctr.predict.RFE.X.Train.glmnet.prob
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2554.0 on 1849 degrees of freedom
## Residual deviance: 1049.4 on 1834 degrees of freedom
## AIC: 1081.4
##
## Number of Fisher Scoring iterations: 6## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## sold.fctr sold.fctr.predict.Final.Ensemble.glm.N
## 1 N 855
## 2 Y 81
## sold.fctr.predict.Final.Ensemble.glm.Y
## 1 140
## 2 774
## Prediction
## Reference N Y
## N 855 140
## Y 81 774
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.805405e-01 7.608659e-01 8.648850e-01 8.949777e-01 5.378378e-01
## AccuracyPValue McnemarPValue
## 4.157594e-221 9.559759e-05
## id
## 1 Final.Ensemble.glm
## feats
## 1 sold.fctr.predict.CSM.X.Train.gbm.prob,sold.fctr.predict.CSM.X.Train.avNNet.prob,sold.fctr.predict.CSM.X.Interact.Train.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.Train.rpart.prob,sold.fctr.predict.CSM.X.center.scale.Train.bagEarth.prob,sold.fctr.predict.CSM.X.Train.bagEarth.prob,sold.fctr.predict.CSM.X.YeoJohnson.Train.bagEarth.prob,sold.fctr.predict.CSM.X.Train.nnet.prob,sold.fctr.predict.CSM.X.Train.glm.prob,sold.fctr.predict.All.X.Train.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.1X1.Train.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X5.Train.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X1.Train.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X3.Train.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.3X5.Train.glmnet.prob,sold.fctr.predict.RFE.X.Train.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.3X3.Train.glmnet.prob,sold.fctr.predict.CSM.X.spatialSign.Train.bagEarth.prob,sold.fctr.predict.Max.cor.Y.rcv.3X1.Train.glmnet.prob,sold.fctr.predict.RFE.X.Train.glm.prob,sold.fctr.predict.Max.cor.Y.Train.rpart.prob,sold.fctr.predict.Low.cor.X.Train.glmnet.prob,sold.fctr.predict.CSM.X.Train.bayesglm.prob,sold.fctr.predict.CSM.X.Train.earth.prob,sold.fctr.predict.Interact.High.cor.Y.Train.glmnet.prob,sold.fctr.predict.CSM.X.Train.rpart.prob
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.737 0.041
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8844339 0.9115578 0.8573099 0.9499204
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.3 0.8750707 0.8819861
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.864885 0.8949777 0.7619022
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.008624645 0.017542
## [1] "fitting model: Final.Ensemble.glmnet"
## [1] " indep_vars: sold.fctr.predict.CSM.X.Train.gbm.prob,sold.fctr.predict.CSM.X.Train.avNNet.prob,sold.fctr.predict.CSM.X.Interact.Train.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.Train.rpart.prob,sold.fctr.predict.CSM.X.center.scale.Train.bagEarth.prob,sold.fctr.predict.CSM.X.Train.bagEarth.prob,sold.fctr.predict.CSM.X.YeoJohnson.Train.bagEarth.prob,sold.fctr.predict.CSM.X.Train.nnet.prob,sold.fctr.predict.CSM.X.Train.glm.prob,sold.fctr.predict.All.X.Train.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.1X1.Train.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X5.Train.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X1.Train.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X3.Train.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.3X5.Train.glmnet.prob,sold.fctr.predict.RFE.X.Train.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.3X3.Train.glmnet.prob,sold.fctr.predict.CSM.X.spatialSign.Train.bagEarth.prob,sold.fctr.predict.Max.cor.Y.rcv.3X1.Train.glmnet.prob,sold.fctr.predict.RFE.X.Train.glm.prob,sold.fctr.predict.Max.cor.Y.Train.rpart.prob,sold.fctr.predict.Low.cor.X.Train.glmnet.prob,sold.fctr.predict.CSM.X.Train.bayesglm.prob,sold.fctr.predict.CSM.X.Train.earth.prob,sold.fctr.predict.Interact.High.cor.Y.Train.glmnet.prob,sold.fctr.predict.CSM.X.Train.rpart.prob"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.55, lambda = 0.000359 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda
## Length Class Mode
## a0 89 -none- numeric
## beta 2314 dgCMatrix S4
## df 89 -none- numeric
## dim 2 -none- numeric
## lambda 89 -none- numeric
## dev.ratio 89 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 26 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## -3.496389e+00
## sold.fctr.predict.All.X.Train.glmnet.prob
## -3.258492e+00
## sold.fctr.predict.CSM.X.Interact.Train.glmnet.prob
## 6.743043e+00
## sold.fctr.predict.CSM.X.Train.avNNet.prob
## 1.200996e+00
## sold.fctr.predict.CSM.X.Train.bagEarth.prob
## 1.911821e+00
## sold.fctr.predict.CSM.X.Train.bayesglm.prob
## -1.615810e+00
## sold.fctr.predict.CSM.X.Train.earth.prob
## -3.515157e+00
## sold.fctr.predict.CSM.X.Train.gbm.prob
## 7.876183e+00
## sold.fctr.predict.CSM.X.Train.glm.prob
## 2.349033e+00
## sold.fctr.predict.CSM.X.Train.nnet.prob
## 1.207224e-01
## sold.fctr.predict.CSM.X.Train.rpart.prob
## -2.901017e+00
## sold.fctr.predict.CSM.X.YeoJohnson.Train.bagEarth.prob
## 1.513707e+00
## sold.fctr.predict.CSM.X.center.scale.Train.bagEarth.prob
## 1.489840e+00
## sold.fctr.predict.CSM.X.spatialSign.Train.bagEarth.prob
## 1.459083e+00
## sold.fctr.predict.Interact.High.cor.Y.Train.glmnet.prob
## -2.214887e+00
## sold.fctr.predict.Low.cor.X.Train.glmnet.prob
## -3.621249e+00
## sold.fctr.predict.Max.cor.Y.Train.rpart.prob
## 1.378762e+00
## sold.fctr.predict.Max.cor.Y.rcv.1X1.Train.glmnet.prob
## 3.206680e-01
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.Train.rpart.prob
## 1.191518e+00
## sold.fctr.predict.Max.cor.Y.rcv.3X1.Train.glmnet.prob
## 1.177185e-01
## sold.fctr.predict.Max.cor.Y.rcv.3X3.Train.glmnet.prob
## 2.416588e-02
## sold.fctr.predict.Max.cor.Y.rcv.3X5.Train.glmnet.prob
## 3.953399e-03
## sold.fctr.predict.Max.cor.Y.rcv.5X1.Train.glmnet.prob
## 5.277230e-04
## sold.fctr.predict.Max.cor.Y.rcv.5X3.Train.glmnet.prob
## 5.931081e-05
## sold.fctr.predict.Max.cor.Y.rcv.5X5.Train.glmnet.prob
## 5.744013e-06
## sold.fctr.predict.RFE.X.Train.glmnet.prob
## -3.282496e+00
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## -3.520474e+00
## sold.fctr.predict.All.X.Train.glmnet.prob
## -3.289709e+00
## sold.fctr.predict.CSM.X.Interact.Train.glmnet.prob
## 6.810529e+00
## sold.fctr.predict.CSM.X.Train.avNNet.prob
## 1.220817e+00
## sold.fctr.predict.CSM.X.Train.bagEarth.prob
## 1.926883e+00
## sold.fctr.predict.CSM.X.Train.bayesglm.prob
## -1.749997e+00
## sold.fctr.predict.CSM.X.Train.earth.prob
## -3.538387e+00
## sold.fctr.predict.CSM.X.Train.gbm.prob
## 7.922205e+00
## sold.fctr.predict.CSM.X.Train.glm.prob
## 2.464248e+00
## sold.fctr.predict.CSM.X.Train.nnet.prob
## 1.361170e-01
## sold.fctr.predict.CSM.X.Train.rpart.prob
## -2.926248e+00
## sold.fctr.predict.CSM.X.YeoJohnson.Train.bagEarth.prob
## 1.515950e+00
## sold.fctr.predict.CSM.X.center.scale.Train.bagEarth.prob
## 1.493144e+00
## sold.fctr.predict.CSM.X.spatialSign.Train.bagEarth.prob
## 1.463067e+00
## sold.fctr.predict.Interact.High.cor.Y.Train.glmnet.prob
## -2.280664e+00
## sold.fctr.predict.Low.cor.X.Train.glmnet.prob
## -3.666065e+00
## sold.fctr.predict.Max.cor.Y.Train.rpart.prob
## 1.396772e+00
## sold.fctr.predict.Max.cor.Y.rcv.1X1.Train.glmnet.prob
## 3.728560e-01
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.Train.rpart.prob
## 1.203495e+00
## sold.fctr.predict.Max.cor.Y.rcv.3X1.Train.glmnet.prob
## 1.466669e-01
## sold.fctr.predict.Max.cor.Y.rcv.3X3.Train.glmnet.prob
## 3.426064e-02
## sold.fctr.predict.Max.cor.Y.rcv.3X5.Train.glmnet.prob
## 6.356257e-03
## sold.fctr.predict.Max.cor.Y.rcv.5X1.Train.glmnet.prob
## 9.654251e-04
## sold.fctr.predict.Max.cor.Y.rcv.5X3.Train.glmnet.prob
## 1.238132e-04
## sold.fctr.predict.Max.cor.Y.rcv.5X5.Train.glmnet.prob
## 1.371529e-05
## sold.fctr.predict.RFE.X.Train.glmnet.prob
## -3.319230e+00
## sold.fctr sold.fctr.predict.Final.Ensemble.glmnet.N
## 1 N 883
## 2 Y 103
## sold.fctr.predict.Final.Ensemble.glmnet.Y
## 1 112
## 2 752
## Prediction
## Reference N Y
## N 883 112
## Y 103 752
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.837838e-01 7.664018e-01 8.682977e-01 8.980408e-01 5.378378e-01
## AccuracyPValue McnemarPValue
## 5.902106e-226 5.853440e-01
## id
## 1 Final.Ensemble.glmnet
## feats
## 1 sold.fctr.predict.CSM.X.Train.gbm.prob,sold.fctr.predict.CSM.X.Train.avNNet.prob,sold.fctr.predict.CSM.X.Interact.Train.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.Train.rpart.prob,sold.fctr.predict.CSM.X.center.scale.Train.bagEarth.prob,sold.fctr.predict.CSM.X.Train.bagEarth.prob,sold.fctr.predict.CSM.X.YeoJohnson.Train.bagEarth.prob,sold.fctr.predict.CSM.X.Train.nnet.prob,sold.fctr.predict.CSM.X.Train.glm.prob,sold.fctr.predict.All.X.Train.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.1X1.Train.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X5.Train.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X1.Train.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.5X3.Train.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.3X5.Train.glmnet.prob,sold.fctr.predict.RFE.X.Train.glmnet.prob,sold.fctr.predict.Max.cor.Y.rcv.3X3.Train.glmnet.prob,sold.fctr.predict.CSM.X.spatialSign.Train.bagEarth.prob,sold.fctr.predict.Max.cor.Y.rcv.3X1.Train.glmnet.prob,sold.fctr.predict.RFE.X.Train.glm.prob,sold.fctr.predict.Max.cor.Y.Train.rpart.prob,sold.fctr.predict.Low.cor.X.Train.glmnet.prob,sold.fctr.predict.CSM.X.Train.bayesglm.prob,sold.fctr.predict.CSM.X.Train.earth.prob,sold.fctr.predict.Interact.High.cor.Y.Train.glmnet.prob,sold.fctr.predict.CSM.X.Train.rpart.prob
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 5.624 0.41
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.8822592 0.9095477 0.8549708 0.9499921
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.4 0.8749273 0.8805434
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8682977 0.8980408 0.7589786
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.008726847 0.0177381rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)## label step_major step_minor label_minor bgn end
## 14 fit.data.training 7 0 0 1519.888 2875.215
## 15 fit.data.training 7 1 1 2875.216 NA
## elapsed
## 14 1355.328
## 15 NA#stop(here"); glb_to_sav()
if (glb_is_classification && glb_is_binomial)
prob_threshold <- glb_models_df[glb_models_df$id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"] else
prob_threshold <- NULL
if (grepl("Ensemble", glb_fin_mdl_id)) {
# Get predictions for each model in ensemble; Outliers that have been moved to OOB might not have been predicted yet
mdlEnsembleComps <- unlist(str_split(subset(glb_models_df,
id == glb_fin_mdl_id)$feats, ","))
if (glb_is_classification && glb_is_binomial)
mdlEnsembleComps <- gsub("\\.prob$", "", mdlEnsembleComps)
mdlEnsembleComps <- gsub(paste0("^",
gsub(".", "\\.", glb_rsp_var_out, fixed = TRUE)),
"", mdlEnsembleComps)
for (mdl_id in mdlEnsembleComps) {
glb_trnobs_df <- glb_get_predictions(df=glb_trnobs_df, mdl_id=mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=prob_threshold)
glb_newobs_df <- glb_get_predictions(df=glb_newobs_df, mdl_id=mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=prob_threshold)
}
}## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(df = glb_newobs_df, mdl_id = mdl_id,
## rsp_var_out = glb_rsp_var_out, : Using default probability threshold: 0.2glb_trnobs_df <- glb_get_predictions(df=glb_trnobs_df, mdl_id=glb_fin_mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=prob_threshold)## Warning in glb_get_predictions(df = glb_trnobs_df, mdl_id =
## glb_fin_mdl_id, : Using default probability threshold: 0.2glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl,
featsimp_df=glb_featsimp_df)
glb_featsimp_df[, paste0(glb_fin_mdl_id, ".importance")] <- glb_featsimp_df$importance
print(glb_featsimp_df)## Ensemble.repeatedcv.glmnet.importance
## sold.fctr.predict.CSM.X.Train.gbm.prob NA
## sold.fctr.predict.CSM.X.Interact.Train.glmnet.prob NA
## sold.fctr.predict.CSM.X.Train.glm.prob NA
## sold.fctr.predict.CSM.X.Train.bagEarth.prob NA
## sold.fctr.predict.CSM.X.YeoJohnson.Train.bagEarth.prob NA
## sold.fctr.predict.CSM.X.center.scale.Train.bagEarth.prob NA
## sold.fctr.predict.CSM.X.spatialSign.Train.bagEarth.prob NA
## sold.fctr.predict.Max.cor.Y.Train.rpart.prob NA
## sold.fctr.predict.CSM.X.Train.avNNet.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.Train.rpart.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.1X1.Train.glmnet.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.3X1.Train.glmnet.prob NA
## sold.fctr.predict.CSM.X.Train.nnet.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.3X3.Train.glmnet.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.3X5.Train.glmnet.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.5X1.Train.glmnet.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.5X3.Train.glmnet.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.5X5.Train.glmnet.prob NA
## sold.fctr.predict.RFE.X.Train.glm.prob NA
## sold.fctr.predict.CSM.X.Train.bayesglm.prob NA
## sold.fctr.predict.Interact.High.cor.Y.Train.glmnet.prob NA
## sold.fctr.predict.CSM.X.Train.rpart.prob NA
## sold.fctr.predict.All.X.Train.glmnet.prob NA
## sold.fctr.predict.RFE.X.Train.glmnet.prob NA
## sold.fctr.predict.CSM.X.Train.earth.prob NA
## sold.fctr.predict.Low.cor.X.Train.glmnet.prob NA
## sold.fctr.predict.All.X.glmnet.prob 44.65453
## sold.fctr.predict.CSM.X.Interact.glmnet.prob 80.98388
## sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.prob 52.29685
## sold.fctr.predict.CSM.X.avNNet.prob 89.06837
## sold.fctr.predict.CSM.X.bagEarth.prob 58.31636
## sold.fctr.predict.CSM.X.bayesglm.prob 23.62630
## sold.fctr.predict.CSM.X.center.scale.bagEarth.prob 58.56381
## sold.fctr.predict.CSM.X.earth.prob 20.08059
## sold.fctr.predict.CSM.X.gbm.prob 100.00000
## sold.fctr.predict.CSM.X.glm.prob 47.22561
## sold.fctr.predict.CSM.X.glmnet.prob 0.00000
## sold.fctr.predict.CSM.X.nnet.prob 48.20355
## sold.fctr.predict.CSM.X.rpart.prob 13.73439
## sold.fctr.predict.CSM.X.spatialSign.bagEarth.prob 31.12100
## sold.fctr.predict.Interact.High.cor.Y.glmnet.prob 19.63196
## sold.fctr.predict.Low.cor.X.glmnet.prob 24.79841
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.prob 67.49716
## sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.prob 40.81531
## sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.prob 30.90820
## sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.prob 31.67832
## sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.prob 33.16895
## sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.prob 36.62686
## sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.prob 36.19640
## sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.prob 39.03268
## sold.fctr.predict.Max.cor.Y.rpart.prob 25.06619
## sold.fctr.predict.RFE.X.glm.prob 30.56269
## sold.fctr.predict.RFE.X.glmnet.prob 32.20199
## importance
## sold.fctr.predict.CSM.X.Train.gbm.prob 100.000000
## sold.fctr.predict.CSM.X.Interact.Train.glmnet.prob 90.272785
## sold.fctr.predict.CSM.X.Train.glm.prob 52.403444
## sold.fctr.predict.CSM.X.Train.bagEarth.prob 48.192617
## sold.fctr.predict.CSM.X.YeoJohnson.Train.bagEarth.prob 44.689156
## sold.fctr.predict.CSM.X.center.scale.Train.bagEarth.prob 44.486844
## sold.fctr.predict.CSM.X.spatialSign.Train.bagEarth.prob 44.223225
## sold.fctr.predict.Max.cor.Y.Train.rpart.prob 43.586503
## sold.fctr.predict.CSM.X.Train.avNNet.prob 42.053938
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.Train.rpart.prob 41.938711
## sold.fctr.predict.Max.cor.Y.rcv.1X1.Train.glmnet.prob 34.563170
## sold.fctr.predict.Max.cor.Y.rcv.3X1.Train.glmnet.prob 32.706678
## sold.fctr.predict.CSM.X.Train.nnet.prob 32.675498
## sold.fctr.predict.Max.cor.Y.rcv.3X3.Train.glmnet.prob 31.816540
## sold.fctr.predict.Max.cor.Y.rcv.3X5.Train.glmnet.prob 31.608949
## sold.fctr.predict.Max.cor.Y.rcv.5X1.Train.glmnet.prob 31.570973
## sold.fctr.predict.Max.cor.Y.rcv.5X3.Train.glmnet.prob 31.565340
## sold.fctr.predict.Max.cor.Y.rcv.5X5.Train.glmnet.prob 31.564637
## sold.fctr.predict.RFE.X.Train.glm.prob 31.564554
## sold.fctr.predict.CSM.X.Train.bayesglm.prob 16.998413
## sold.fctr.predict.Interact.High.cor.Y.Train.glmnet.prob 12.096600
## sold.fctr.predict.CSM.X.Train.rpart.prob 6.322930
## sold.fctr.predict.All.X.Train.glmnet.prob 3.200415
## sold.fctr.predict.RFE.X.Train.glmnet.prob 2.969153
## sold.fctr.predict.CSM.X.Train.earth.prob 1.010319
## sold.fctr.predict.Low.cor.X.Train.glmnet.prob 0.000000
## sold.fctr.predict.All.X.glmnet.prob NA
## sold.fctr.predict.CSM.X.Interact.glmnet.prob NA
## sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.prob NA
## sold.fctr.predict.CSM.X.avNNet.prob NA
## sold.fctr.predict.CSM.X.bagEarth.prob NA
## sold.fctr.predict.CSM.X.bayesglm.prob NA
## sold.fctr.predict.CSM.X.center.scale.bagEarth.prob NA
## sold.fctr.predict.CSM.X.earth.prob NA
## sold.fctr.predict.CSM.X.gbm.prob NA
## sold.fctr.predict.CSM.X.glm.prob NA
## sold.fctr.predict.CSM.X.glmnet.prob NA
## sold.fctr.predict.CSM.X.nnet.prob NA
## sold.fctr.predict.CSM.X.rpart.prob NA
## sold.fctr.predict.CSM.X.spatialSign.bagEarth.prob NA
## sold.fctr.predict.Interact.High.cor.Y.glmnet.prob NA
## sold.fctr.predict.Low.cor.X.glmnet.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.prob NA
## sold.fctr.predict.Max.cor.Y.rpart.prob NA
## sold.fctr.predict.RFE.X.glm.prob NA
## sold.fctr.predict.RFE.X.glmnet.prob NA
## Final.Ensemble.glmnet.importance
## sold.fctr.predict.CSM.X.Train.gbm.prob 100.000000
## sold.fctr.predict.CSM.X.Interact.Train.glmnet.prob 90.272785
## sold.fctr.predict.CSM.X.Train.glm.prob 52.403444
## sold.fctr.predict.CSM.X.Train.bagEarth.prob 48.192617
## sold.fctr.predict.CSM.X.YeoJohnson.Train.bagEarth.prob 44.689156
## sold.fctr.predict.CSM.X.center.scale.Train.bagEarth.prob 44.486844
## sold.fctr.predict.CSM.X.spatialSign.Train.bagEarth.prob 44.223225
## sold.fctr.predict.Max.cor.Y.Train.rpart.prob 43.586503
## sold.fctr.predict.CSM.X.Train.avNNet.prob 42.053938
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.Train.rpart.prob 41.938711
## sold.fctr.predict.Max.cor.Y.rcv.1X1.Train.glmnet.prob 34.563170
## sold.fctr.predict.Max.cor.Y.rcv.3X1.Train.glmnet.prob 32.706678
## sold.fctr.predict.CSM.X.Train.nnet.prob 32.675498
## sold.fctr.predict.Max.cor.Y.rcv.3X3.Train.glmnet.prob 31.816540
## sold.fctr.predict.Max.cor.Y.rcv.3X5.Train.glmnet.prob 31.608949
## sold.fctr.predict.Max.cor.Y.rcv.5X1.Train.glmnet.prob 31.570973
## sold.fctr.predict.Max.cor.Y.rcv.5X3.Train.glmnet.prob 31.565340
## sold.fctr.predict.Max.cor.Y.rcv.5X5.Train.glmnet.prob 31.564637
## sold.fctr.predict.RFE.X.Train.glm.prob 31.564554
## sold.fctr.predict.CSM.X.Train.bayesglm.prob 16.998413
## sold.fctr.predict.Interact.High.cor.Y.Train.glmnet.prob 12.096600
## sold.fctr.predict.CSM.X.Train.rpart.prob 6.322930
## sold.fctr.predict.All.X.Train.glmnet.prob 3.200415
## sold.fctr.predict.RFE.X.Train.glmnet.prob 2.969153
## sold.fctr.predict.CSM.X.Train.earth.prob 1.010319
## sold.fctr.predict.Low.cor.X.Train.glmnet.prob 0.000000
## sold.fctr.predict.All.X.glmnet.prob NA
## sold.fctr.predict.CSM.X.Interact.glmnet.prob NA
## sold.fctr.predict.CSM.X.YeoJohnson.bagEarth.prob NA
## sold.fctr.predict.CSM.X.avNNet.prob NA
## sold.fctr.predict.CSM.X.bagEarth.prob NA
## sold.fctr.predict.CSM.X.bayesglm.prob NA
## sold.fctr.predict.CSM.X.center.scale.bagEarth.prob NA
## sold.fctr.predict.CSM.X.earth.prob NA
## sold.fctr.predict.CSM.X.gbm.prob NA
## sold.fctr.predict.CSM.X.glm.prob NA
## sold.fctr.predict.CSM.X.glmnet.prob NA
## sold.fctr.predict.CSM.X.nnet.prob NA
## sold.fctr.predict.CSM.X.rpart.prob NA
## sold.fctr.predict.CSM.X.spatialSign.bagEarth.prob NA
## sold.fctr.predict.Interact.High.cor.Y.glmnet.prob NA
## sold.fctr.predict.Low.cor.X.glmnet.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.rpart.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.1X1.glmnet.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.3X1.glmnet.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.3X3.glmnet.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.3X5.glmnet.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.5X1.glmnet.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.5X3.glmnet.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.5X5.glmnet.prob NA
## sold.fctr.predict.Max.cor.Y.rpart.prob NA
## sold.fctr.predict.RFE.X.glm.prob NA
## sold.fctr.predict.RFE.X.glmnet.prob NAif (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id,
prob_threshold=glb_models_df[glb_models_df$id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_trnobs_df, mdl_id =
## glb_fin_mdl_id, : No features in selected model are statistically importantdsp_feats_vctr <- c(NULL)
for(var in grep(".importance", names(glb_feats_df), fixed=TRUE, value=TRUE))
dsp_feats_vctr <- union(dsp_feats_vctr,
glb_feats_df[!is.na(glb_feats_df[, var]), "id"])
# print(glb_trnobs_df[glb_trnobs_df$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glb_trnobs_df), value=TRUE)])
print(setdiff(names(glb_trnobs_df), names(glb_allobs_df)))## [1] "sold.fctr.predict.CSM.X.Train.gbm.prob"
## [2] "sold.fctr.predict.CSM.X.Train.gbm"
## [3] "sold.fctr.predict.CSM.X.Train.gbm.err"
## [4] "sold.fctr.predict.CSM.X.Train.gbm.err.abs"
## [5] "sold.fctr.predict.CSM.X.Train.gbm.accurate"
## [6] "sold.fctr.predict.CSM.X.Train.avNNet.prob"
## [7] "sold.fctr.predict.CSM.X.Train.avNNet"
## [8] "sold.fctr.predict.CSM.X.Train.avNNet.err"
## [9] "sold.fctr.predict.CSM.X.Train.avNNet.err.abs"
## [10] "sold.fctr.predict.CSM.X.Train.avNNet.accurate"
## [11] "sold.fctr.predict.CSM.X.Interact.Train.glmnet.prob"
## [12] "sold.fctr.predict.CSM.X.Interact.Train.glmnet"
## [13] "sold.fctr.predict.CSM.X.Interact.Train.glmnet.err"
## [14] "sold.fctr.predict.CSM.X.Interact.Train.glmnet.err.abs"
## [15] "sold.fctr.predict.CSM.X.Interact.Train.glmnet.accurate"
## [16] "sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.Train.rpart.prob"
## [17] "sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.Train.rpart"
## [18] "sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.Train.rpart.err"
## [19] "sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.Train.rpart.err.abs"
## [20] "sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.Train.rpart.accurate"
## [21] "sold.fctr.predict.CSM.X.center.scale.Train.bagEarth.prob"
## [22] "sold.fctr.predict.CSM.X.center.scale.Train.bagEarth"
## [23] "sold.fctr.predict.CSM.X.center.scale.Train.bagEarth.err"
## [24] "sold.fctr.predict.CSM.X.center.scale.Train.bagEarth.err.abs"
## [25] "sold.fctr.predict.CSM.X.center.scale.Train.bagEarth.accurate"
## [26] "sold.fctr.predict.CSM.X.Train.bagEarth.prob"
## [27] "sold.fctr.predict.CSM.X.Train.bagEarth"
## [28] "sold.fctr.predict.CSM.X.Train.bagEarth.err"
## [29] "sold.fctr.predict.CSM.X.Train.bagEarth.err.abs"
## [30] "sold.fctr.predict.CSM.X.Train.bagEarth.accurate"
## [31] "sold.fctr.predict.CSM.X.YeoJohnson.Train.bagEarth.prob"
## [32] "sold.fctr.predict.CSM.X.YeoJohnson.Train.bagEarth"
## [33] "sold.fctr.predict.CSM.X.YeoJohnson.Train.bagEarth.err"
## [34] "sold.fctr.predict.CSM.X.YeoJohnson.Train.bagEarth.err.abs"
## [35] "sold.fctr.predict.CSM.X.YeoJohnson.Train.bagEarth.accurate"
## [36] "sold.fctr.predict.CSM.X.Train.nnet.prob"
## [37] "sold.fctr.predict.CSM.X.Train.nnet"
## [38] "sold.fctr.predict.CSM.X.Train.nnet.err"
## [39] "sold.fctr.predict.CSM.X.Train.nnet.err.abs"
## [40] "sold.fctr.predict.CSM.X.Train.nnet.accurate"
## [41] "sold.fctr.predict.CSM.X.Train.glm.prob"
## [42] "sold.fctr.predict.CSM.X.Train.glm"
## [43] "sold.fctr.predict.CSM.X.Train.glm.err"
## [44] "sold.fctr.predict.CSM.X.Train.glm.err.abs"
## [45] "sold.fctr.predict.CSM.X.Train.glm.accurate"
## [46] "sold.fctr.predict.All.X.Train.glmnet.prob"
## [47] "sold.fctr.predict.All.X.Train.glmnet"
## [48] "sold.fctr.predict.All.X.Train.glmnet.err"
## [49] "sold.fctr.predict.All.X.Train.glmnet.err.abs"
## [50] "sold.fctr.predict.All.X.Train.glmnet.accurate"
## [51] "sold.fctr.predict.Max.cor.Y.rcv.1X1.Train.glmnet.prob"
## [52] "sold.fctr.predict.Max.cor.Y.rcv.1X1.Train.glmnet"
## [53] "sold.fctr.predict.Max.cor.Y.rcv.1X1.Train.glmnet.err"
## [54] "sold.fctr.predict.Max.cor.Y.rcv.1X1.Train.glmnet.err.abs"
## [55] "sold.fctr.predict.Max.cor.Y.rcv.1X1.Train.glmnet.accurate"
## [56] "sold.fctr.predict.Max.cor.Y.rcv.5X5.Train.glmnet.prob"
## [57] "sold.fctr.predict.Max.cor.Y.rcv.5X5.Train.glmnet"
## [58] "sold.fctr.predict.Max.cor.Y.rcv.5X5.Train.glmnet.err"
## [59] "sold.fctr.predict.Max.cor.Y.rcv.5X5.Train.glmnet.err.abs"
## [60] "sold.fctr.predict.Max.cor.Y.rcv.5X5.Train.glmnet.accurate"
## [61] "sold.fctr.predict.Max.cor.Y.rcv.5X1.Train.glmnet.prob"
## [62] "sold.fctr.predict.Max.cor.Y.rcv.5X1.Train.glmnet"
## [63] "sold.fctr.predict.Max.cor.Y.rcv.5X1.Train.glmnet.err"
## [64] "sold.fctr.predict.Max.cor.Y.rcv.5X1.Train.glmnet.err.abs"
## [65] "sold.fctr.predict.Max.cor.Y.rcv.5X1.Train.glmnet.accurate"
## [66] "sold.fctr.predict.Max.cor.Y.rcv.5X3.Train.glmnet.prob"
## [67] "sold.fctr.predict.Max.cor.Y.rcv.5X3.Train.glmnet"
## [68] "sold.fctr.predict.Max.cor.Y.rcv.5X3.Train.glmnet.err"
## [69] "sold.fctr.predict.Max.cor.Y.rcv.5X3.Train.glmnet.err.abs"
## [70] "sold.fctr.predict.Max.cor.Y.rcv.5X3.Train.glmnet.accurate"
## [71] "sold.fctr.predict.Max.cor.Y.rcv.3X5.Train.glmnet.prob"
## [72] "sold.fctr.predict.Max.cor.Y.rcv.3X5.Train.glmnet"
## [73] "sold.fctr.predict.Max.cor.Y.rcv.3X5.Train.glmnet.err"
## [74] "sold.fctr.predict.Max.cor.Y.rcv.3X5.Train.glmnet.err.abs"
## [75] "sold.fctr.predict.Max.cor.Y.rcv.3X5.Train.glmnet.accurate"
## [76] "sold.fctr.predict.RFE.X.Train.glmnet.prob"
## [77] "sold.fctr.predict.RFE.X.Train.glmnet"
## [78] "sold.fctr.predict.RFE.X.Train.glmnet.err"
## [79] "sold.fctr.predict.RFE.X.Train.glmnet.err.abs"
## [80] "sold.fctr.predict.RFE.X.Train.glmnet.accurate"
## [81] "sold.fctr.predict.Max.cor.Y.rcv.3X3.Train.glmnet.prob"
## [82] "sold.fctr.predict.Max.cor.Y.rcv.3X3.Train.glmnet"
## [83] "sold.fctr.predict.Max.cor.Y.rcv.3X3.Train.glmnet.err"
## [84] "sold.fctr.predict.Max.cor.Y.rcv.3X3.Train.glmnet.err.abs"
## [85] "sold.fctr.predict.Max.cor.Y.rcv.3X3.Train.glmnet.accurate"
## [86] "sold.fctr.predict.CSM.X.spatialSign.Train.bagEarth.prob"
## [87] "sold.fctr.predict.CSM.X.spatialSign.Train.bagEarth"
## [88] "sold.fctr.predict.CSM.X.spatialSign.Train.bagEarth.err"
## [89] "sold.fctr.predict.CSM.X.spatialSign.Train.bagEarth.err.abs"
## [90] "sold.fctr.predict.CSM.X.spatialSign.Train.bagEarth.accurate"
## [91] "sold.fctr.predict.Max.cor.Y.rcv.3X1.Train.glmnet.prob"
## [92] "sold.fctr.predict.Max.cor.Y.rcv.3X1.Train.glmnet"
## [93] "sold.fctr.predict.Max.cor.Y.rcv.3X1.Train.glmnet.err"
## [94] "sold.fctr.predict.Max.cor.Y.rcv.3X1.Train.glmnet.err.abs"
## [95] "sold.fctr.predict.Max.cor.Y.rcv.3X1.Train.glmnet.accurate"
## [96] "sold.fctr.predict.RFE.X.Train.glm.prob"
## [97] "sold.fctr.predict.RFE.X.Train.glm"
## [98] "sold.fctr.predict.RFE.X.Train.glm.err"
## [99] "sold.fctr.predict.RFE.X.Train.glm.err.abs"
## [100] "sold.fctr.predict.RFE.X.Train.glm.accurate"
## [101] "sold.fctr.predict.Max.cor.Y.Train.rpart.prob"
## [102] "sold.fctr.predict.Max.cor.Y.Train.rpart"
## [103] "sold.fctr.predict.Max.cor.Y.Train.rpart.err"
## [104] "sold.fctr.predict.Max.cor.Y.Train.rpart.err.abs"
## [105] "sold.fctr.predict.Max.cor.Y.Train.rpart.accurate"
## [106] "sold.fctr.predict.Low.cor.X.Train.glmnet.prob"
## [107] "sold.fctr.predict.Low.cor.X.Train.glmnet"
## [108] "sold.fctr.predict.Low.cor.X.Train.glmnet.err"
## [109] "sold.fctr.predict.Low.cor.X.Train.glmnet.err.abs"
## [110] "sold.fctr.predict.Low.cor.X.Train.glmnet.accurate"
## [111] "sold.fctr.predict.CSM.X.Train.bayesglm.prob"
## [112] "sold.fctr.predict.CSM.X.Train.bayesglm"
## [113] "sold.fctr.predict.CSM.X.Train.bayesglm.err"
## [114] "sold.fctr.predict.CSM.X.Train.bayesglm.err.abs"
## [115] "sold.fctr.predict.CSM.X.Train.bayesglm.accurate"
## [116] "sold.fctr.predict.CSM.X.Train.earth.prob"
## [117] "sold.fctr.predict.CSM.X.Train.earth"
## [118] "sold.fctr.predict.CSM.X.Train.earth.err"
## [119] "sold.fctr.predict.CSM.X.Train.earth.err.abs"
## [120] "sold.fctr.predict.CSM.X.Train.earth.accurate"
## [121] "sold.fctr.predict.Interact.High.cor.Y.Train.glmnet.prob"
## [122] "sold.fctr.predict.Interact.High.cor.Y.Train.glmnet"
## [123] "sold.fctr.predict.Interact.High.cor.Y.Train.glmnet.err"
## [124] "sold.fctr.predict.Interact.High.cor.Y.Train.glmnet.err.abs"
## [125] "sold.fctr.predict.Interact.High.cor.Y.Train.glmnet.accurate"
## [126] "sold.fctr.predict.CSM.X.Train.rpart.prob"
## [127] "sold.fctr.predict.CSM.X.Train.rpart"
## [128] "sold.fctr.predict.CSM.X.Train.rpart.err"
## [129] "sold.fctr.predict.CSM.X.Train.rpart.err.abs"
## [130] "sold.fctr.predict.CSM.X.Train.rpart.accurate"
## [131] "sold.fctr.predict.Final.Ensemble.glmnet.prob"
## [132] "sold.fctr.predict.Final.Ensemble.glmnet"
## [133] "sold.fctr.predict.Final.Ensemble.glmnet.err"
## [134] "sold.fctr.predict.Final.Ensemble.glmnet.err.abs"
## [135] "sold.fctr.predict.Final.Ensemble.glmnet.accurate"for (col in setdiff(names(glb_trnobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.src == "Train", col] <- glb_trnobs_df[, col]
print(setdiff(names(glb_fitobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))## character(0)for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df, glb_allobs_df,
#glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file=paste0(glb_out_pfx, "dsk.RData"))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all.prediction","model.final")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
## 3.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: data.training.all.prediction
## 4.0000 5 0 1 1 1
## 4.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: model.final
## 5.0000 4 0 0 2 1
glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc=TRUE)## label step_major step_minor label_minor bgn end
## 15 fit.data.training 7 1 1 2875.216 2943.585
## 16 predict.data.new 8 0 0 2943.586 NA
## elapsed
## 15 68.369
## 16 NAStep 8.0: predict data new
# Compute final model predictions
#glb_to_sav(); all.equal(sav_allobs_df, glb_allobs_df); all.equal(sav_trnobs_df, glb_trnobs_df); all.equal(sav_newobs_df, glb_newobs_df)
if (glb_is_classification && glb_is_binomial)
prob_threshold_def <-
glb_models_df[glb_models_df$id == glb_sel_mdl_id, "opt.prob.threshold.OOB"] else
prob_threshold_def <- NULL
for (obsSet in c("trn", "new")) {
obs_df <- switch(obsSet, all = glb_allobs_df, trn = glb_trnobs_df, new = glb_newobs_df)
obs_df <- glb_get_predictions(obs_df, mdl_id = glb_fin_mdl_id,
rsp_var_out = glb_rsp_var_out, prob_threshold_def = prob_threshold_def)
if (obsSet == "all") glb_allobs_df <- obs_df else
if (obsSet == "trn") glb_trnobs_df <- obs_df else
if (obsSet == "new") glb_newobs_df <- obs_df
}## Warning in glb_get_predictions(obs_df, mdl_id = glb_fin_mdl_id, rsp_var_out
## = glb_rsp_var_out, : Using default probability threshold: 0.2## Warning in glb_get_predictions(obs_df, mdl_id = glb_fin_mdl_id, rsp_var_out
## = glb_rsp_var_out, : Using default probability threshold: 0.2rm(obs_df)
glb_allobs_df <- orderBy(reformulate(glb_id_var), myrbind_df(glb_trnobs_df, glb_newobs_df))
glb_analytics_diag_plots(obs_df = glb_newobs_df, mdl_id = glb_fin_mdl_id,
prob_threshold = prob_threshold_def)## Warning in glb_analytics_diag_plots(obs_df = glb_newobs_df, mdl_id =
## glb_fin_mdl_id, : No features in selected model are statistically importantif (is.null(glb_out_obs)) obs_df <- glb_newobs_df else
obs_df <- switch(glb_out_obs,
all = glb_allobs_df, trn = glb_trnobs_df, new = glb_newobs_df)
require(stringr)
if (glb_is_classification && glb_is_binomial) {
# submit_df <- glb_newobs_df[, c(glb_id_var,
# paste0(glb_rsp_var_out, glb_fin_mdl_id, ".prob"))]
# names(submit_df)[2] <- "Probability1"
obsout_df <- obs_df[, glb_id_var, FALSE]
for (clmn in names(glb_out_vars_lst))
if (!grepl("^%<d-%", glb_out_vars_lst[[clmn]]))
obsout_df[, clmn] <- obs_df[, glb_out_vars_lst[[clmn]]] else {
feat <- str_trim(unlist(strsplit(glb_out_vars_lst[[clmn]], "%<d-%"))[2])
obsout_df[, clmn] <- obs_df[, eval(parse(text = feat))]
}
glb_force_prediction_lst <- list()
glb_force_prediction_lst[["0"]] <- c(11885, 11907, 11932, 11943,
12050, 12115, 12171,
12253, 12285, 12367, 12388, 12399,
12585)
for (obs_id in glb_force_prediction_lst[["0"]]) {
if (sum(glb_allobs_df[, glb_id_var] == obs_id) == 0)
next
if (is.na(glb_allobs_df[glb_allobs_df[, glb_id_var] == obs_id, ".grpid"]))
stop(".grpid is NA")
# submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] <-
# max(0, submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] - 0.5)
}
glb_force_prediction_lst[["1"]] <- c(11871, 11875, 11886,
11913, 11931, 11937, 11967, 11982, 11990, 11991, 11994, 11999,
12000, 12002, 12004, 12018, 12021, 12065, 12072,
12111, 12114, 12126, 12134, 12152, 12172,
12213, 12214, 12233, 12265, 12278, 12299,
12446, 12491,
12505, 12576, 12608, 12630)
for (obs_id in glb_force_prediction_lst[["1"]]) {
if (sum(glb_allobs_df[, glb_id_var] == obs_id) == 0)
next
if (is.na(glb_allobs_df[glb_allobs_df[, glb_id_var] == obs_id, ".grpid"]))
stop(".grpid is NA")
# submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] <-
# min(0.9999, submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] + 0.5)
}
rsp_var_out <- paste0(glb_rsp_var_out, glb_fin_mdl_id)
for (obs_id in glb_newobs_df[!is.na(glb_newobs_df[, rsp_var_out]) &
(glb_newobs_df[, rsp_var_out] == "Y") &
(glb_newobs_df[ , "startprice"] > 675), "UniqueID"]) {
# submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] <-
# max(0, submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] - 0.5)
}
} else {
# submit_df <- glb_newobs_df[, c(glb_id_var,
# paste0(glb_rsp_var_out, glb_fin_mdl_id))]
obsout_df <- obs_df[, glb_id_var, FALSE]
for (clmn in names(glb_out_vars_lst))
if (!grepl("^%<d-%", glb_out_vars_lst[[clmn]]))
obsout_df[, clmn] <- obs_df[, glb_out_vars_lst[[clmn]]] else {
feat <- str_trim(unlist(strsplit(glb_out_vars_lst[[clmn]], "%<d-%"))[2])
obsout_df[, clmn] <- obs_df[, eval(parse(text=feat))]
}
}
if (glb_is_classification) {
rsp_var_out <- paste0(glb_rsp_var_out, glb_fin_mdl_id)
tmp_newobs_df <- subset(glb_newobs_df[, c(glb_id_var, ".grpid", rsp_var_out)],
!is.na(.grpid))
tmp_newobs_df <- merge(tmp_newobs_df, dupgrps_df, by = ".grpid", all.x = TRUE)
tmp_newobs_df <- merge(tmp_newobs_df, obsout_df, by = glb_id_var, all.x = TRUE)
tmp_newobs_df$.err <-
((tmp_newobs_df$Probability1 > 0.5) & (tmp_newobs_df$sold.0 > 0) |
(tmp_newobs_df$Probability1 < 0.5) & (tmp_newobs_df$sold.1 > 0))
tmp_newobs_df <- orderBy(~UniqueID, subset(tmp_newobs_df, .err == TRUE))
print(sprintf("Prediction errors in duplicates: %d", nrow(tmp_newobs_df)))
print(tmp_newobs_df)
# if (nrow(tmp_newobs_df) > 0)
# stop("check Prediction errors in duplicates")
#print(dupobs_df[dupobs_df$.grpid == 26, ])
tmp_newobs_df <- cbind(glb_newobs_df, obsout_df[, "Probability1", FALSE])
# if (max(glb_newobs_df[!is.na(glb_newobs_df[, rsp_var_out]) &
# (tmp_newobs_df[, "Probability1"] >= 0.5), "startprice"]) >
# max(glb_allobs_df[!is.na(glb_allobs_df[, glb_rsp_var]) &
# (glb_allobs_df[, glb_rsp_var] == "Y"), "startprice"]))
# stop("startprice for some +ve predictions > 675")
# Check predictions that are outside of data ranges
#stop(here")
require(stringr)
tmp_feats_df <- subset(glb_feats_df,
!nzv &
(exclude.as.feat != 1) &
!grepl(".fctr", id, fixed=TRUE))[, "id", FALSE]
ranges_all_df <- glb_allobs_df[, tmp_feats_df$id] %>%
dplyr::summarise_each(funs(min(., na.rm=TRUE),
max(., na.rm=TRUE))) %>%
tidyr::gather() %>%
dplyr::mutate(id=str_sub(key, 1, -5),
stat=str_sub(key, -3)) %>%
dplyr::select(-key) %>%
tidyr::spread(stat, value)
# sav_ranges_trn_df <- ranges_trn_df; all.equal(sav_ranges_trn_df, ranges_trn_df)
# sav_ranges_new_df <- ranges_new_df; all.equal(sav_ranges_new_df, ranges_new_df)
get_ranges_df <- function(obs_df, feats, class_var) {
require(tidyr)
ranges_df <- obs_df[, c(class_var, feats)] %>%
dplyr::group_by_(class_var) %>%
dplyr::summarise_each(funs(min(., na.rm=TRUE),
max(., na.rm=TRUE))) %>%
tidyr::gather(key, value, -1) %>%
mutate(id=str_sub(key, 1, -5),
stat.vname=paste0(str_sub(key, -3), ".", class_var)) %>%
unite_("stat.class", c("stat.vname", class_var), sep=".") %>%
dplyr::select(-key) %>%
spread(stat.class, value)
return(ranges_df)
}
rsp_var_out_OOB <- paste0(glb_rsp_var_out, glb_sel_mdl_id)
rsp_var_out_new <- paste0(glb_rsp_var_out, glb_fin_mdl_id)
ranges_trn_df <- get_ranges_df(obs_df=glb_trnobs_df, feats=tmp_feats_df$id,
class_var=glb_rsp_var)
ranges_fit_df <- get_ranges_df(obs_df=glb_fitobs_df, feats=tmp_feats_df$id,
class_var=glb_rsp_var)
ranges_OOB_df <- get_ranges_df(obs_df=glb_OOBobs_df, feats=tmp_feats_df$id,
class_var=rsp_var_out_OOB)
ranges_new_df <- get_ranges_df(obs_df=glb_newobs_df, feats=tmp_feats_df$id,
class_var=rsp_var_out_new)
for (obsset in c("OOB", "new")) {
if (obsset == "OOB") {
ranges_ref_df <- ranges_fit_df; obs_df <- glb_OOBobs_df;
rsp_var_out_obs <- rsp_var_out_OOB; sprintf_pfx <- "OOBobs";
} else {
ranges_ref_df <- ranges_trn_df; obs_df <- glb_newobs_df;
rsp_var_out_obs <- rsp_var_out_new; sprintf_pfx <- "newobs";
}
plt_feats_df <- glb_feats_df %>%
merge(ranges_all_df, all=TRUE) %>%
merge(ranges_ref_df, all=TRUE) %>%
merge(ranges_OOB_df, all=TRUE) %>%
merge(ranges_new_df, all=TRUE) %>%
subset(!is.na(min) & (id != ".rnorm"))
row.names(plt_feats_df) <- plt_feats_df$id
range_outlier_ids <- c(NULL)
for (clss in unique(obs_df[, rsp_var_out_obs])) {
for (stat in c("min", "max")) {
if (stat == "min") {
dsp_feats <- plt_feats_df[
which(plt_feats_df[, paste("min", rsp_var_out_obs, clss, sep=".")] <
plt_feats_df[, paste("min", glb_rsp_var, clss, sep=".")]), "id"]
} else {
dsp_feats <- plt_feats_df[
which(plt_feats_df[, paste("max", rsp_var_out_obs, clss, sep=".")] >
plt_feats_df[, paste("max", glb_rsp_var, clss, sep=".")]), "id"]
}
if (length(dsp_feats) > 0) {
ths_ids <- c(NULL)
for (feat in dsp_feats) {
if (stat == "min") {
ths_ids <- union(ths_ids,
obs_df[(obs_df[, rsp_var_out_obs] == clss) &
(obs_df[, feat] <
plt_feats_df[plt_feats_df$id == feat, paste("min", glb_rsp_var, clss, sep=".")]),
glb_id_var])
} else {
ths_ids <- union(ths_ids,
obs_df[(obs_df[, rsp_var_out_obs] == clss) &
(obs_df[, feat] >
plt_feats_df[plt_feats_df$id == feat, paste("max", glb_rsp_var, clss, sep=".")]),
glb_id_var])
}
}
tmp_obs_df <- obs_df[obs_df[, glb_id_var] %in% ths_ids,
c(glb_id_var, rsp_var_out_obs, dsp_feats)]
if (stat == "min") {
print(sprintf("%s %s %s: min < min of Train range: %d",
sprintf_pfx, rsp_var_out_obs, clss, nrow(tmp_obs_df)))
} else {
print(sprintf("%s %s %s: max > max of Train range: %d",
sprintf_pfx, rsp_var_out_obs, clss, nrow(tmp_obs_df)))
}
myprint_df(tmp_obs_df)
print(subset(plt_feats_df, id %in% dsp_feats))
range_outlier_ids <- union(range_outlier_ids, ths_ids)
}
}
}
print(sprintf("%s total range outliers: %d", sprintf_pfx, length(range_outlier_ids)))
}
} ## [1] "Prediction errors in duplicates: 10"
## UniqueID .grpid sold.fctr.predict.Final.Ensemble.glmnet sold.0 sold.1
## 1 11871 24 Y 0 2
## 3 11885 26 Y 1 0
## 19 11982 33 N 0 1
## 24 11999 115 Y 0 1
## 42 12111 128 Y 0 1
## 55 12214 118 Y 0 1
## 76 12446 103 N 0 1
## 80 12505 54 Y 0 1
## 81 12576 93 N 0 1
## 87 12630 76 Y 0 2
## sold.NA .freq Probability1 .err
## 1 1 3 0.49540494 TRUE
## 3 1 2 0.51254614 TRUE
## 19 1 2 0.12434759 TRUE
## 24 1 2 0.36996122 TRUE
## 42 1 2 0.41562392 TRUE
## 55 1 2 0.48588986 TRUE
## 76 1 2 0.05282579 TRUE
## 80 1 2 0.49195655 TRUE
## 81 1 2 0.03408905 TRUE
## 87 1 3 0.24553146 TRUE
## [1] "OOBobs sold.fctr.predict.Ensemble.repeatedcv.glmnet N: max > max of Train range: 5"
## UniqueID sold.fctr.predict.Ensemble.repeatedcv.glmnet
## 1382 11389 N
## 1294 11301 N
## 1117 11123 N
## 1633 11642 N
## 1767 11776 N
## D.chrs.uppr.n.log D.ratio.weight.sum.wrds.n D.weight.post.stem.sum
## 1382 2.484907 6.2832246 6.283225
## 1294 4.465908 0.3479012 4.522716
## 1117 4.343805 0.4265605 6.824967
## 1633 2.708050 2.3057474 6.917242
## 1767 3.850148 0.4838468 7.257702
## D.weight.post.stop.sum D.weight.sum
## 1382 6.283225 6.283225
## 1294 5.268255 4.522716
## 1117 7.020585 6.824967
## 1633 6.917242 6.917242
## 1767 7.278648 7.257702
## id cor.y
## D.chrs.uppr.n.log D.chrs.uppr.n.log -0.054491610
## D.ratio.weight.sum.wrds.n D.ratio.weight.sum.wrds.n -0.001080251
## D.weight.post.stem.sum D.weight.post.stem.sum -0.047105442
## D.weight.post.stop.sum D.weight.post.stop.sum -0.045009208
## D.weight.sum D.weight.sum -0.047105442
## exclude.as.feat cor.y.abs
## D.chrs.uppr.n.log FALSE 0.054491610
## D.ratio.weight.sum.wrds.n FALSE 0.001080251
## D.weight.post.stem.sum FALSE 0.047105442
## D.weight.post.stop.sum FALSE 0.045009208
## D.weight.sum FALSE 0.047105442
## cor.high.X freqRatio percentUnique
## D.chrs.uppr.n.log D.chrs.n.log 15.66176 4.486486
## D.ratio.weight.sum.wrds.n <NA> 62.70588 34.810811
## D.weight.post.stem.sum D.terms.post.stop.n.log 62.70588 34.648649
## D.weight.post.stop.sum D.weight.post.stem.sum 62.70588 34.756757
## D.weight.sum D.weight.post.stem.sum 62.70588 34.648649
## zeroVar nzv is.cor.y.abs.low interaction.feat
## D.chrs.uppr.n.log FALSE FALSE FALSE <NA>
## D.ratio.weight.sum.wrds.n FALSE FALSE TRUE <NA>
## D.weight.post.stem.sum FALSE FALSE FALSE <NA>
## D.weight.post.stop.sum FALSE FALSE FALSE <NA>
## D.weight.sum FALSE FALSE FALSE <NA>
## shapiro.test.p.value rsp_var_raw id_var rsp_var
## D.chrs.uppr.n.log 4.550442e-50 FALSE NA NA
## D.ratio.weight.sum.wrds.n 1.286452e-59 FALSE NA NA
## D.weight.post.stem.sum 2.197748e-48 FALSE NA NA
## D.weight.post.stop.sum 2.648991e-48 FALSE NA NA
## D.weight.sum 2.197748e-48 FALSE NA NA
## max min max.sold.fctr.N max.sold.fctr.Y
## D.chrs.uppr.n.log 4.465908 0 4.454347 4.454347
## D.ratio.weight.sum.wrds.n 7.048759 0 5.241404 7.048759
## D.weight.post.stem.sum 8.048759 0 6.702992 7.048759
## D.weight.post.stop.sum 8.563332 0 7.202321 7.048759
## D.weight.sum 8.048759 0 6.702992 7.048759
## min.sold.fctr.N min.sold.fctr.Y
## D.chrs.uppr.n.log 0 0
## D.ratio.weight.sum.wrds.n 0 0
## D.weight.post.stem.sum 0 0
## D.weight.post.stop.sum 0 0
## D.weight.sum 0 0
## max.sold.fctr.predict.Ensemble.repeatedcv.glmnet.N
## D.chrs.uppr.n.log 4.465908
## D.ratio.weight.sum.wrds.n 6.283225
## D.weight.post.stem.sum 7.257702
## D.weight.post.stop.sum 7.278648
## D.weight.sum 7.257702
## max.sold.fctr.predict.Ensemble.repeatedcv.glmnet.Y
## D.chrs.uppr.n.log 4.442651
## D.ratio.weight.sum.wrds.n 5.241404
## D.weight.post.stem.sum 8.048759
## D.weight.post.stop.sum 8.563332
## D.weight.sum 8.048759
## min.sold.fctr.predict.Ensemble.repeatedcv.glmnet.N
## D.chrs.uppr.n.log 0
## D.ratio.weight.sum.wrds.n 0
## D.weight.post.stem.sum 0
## D.weight.post.stop.sum 0
## D.weight.sum 0
## min.sold.fctr.predict.Ensemble.repeatedcv.glmnet.Y
## D.chrs.uppr.n.log 0
## D.ratio.weight.sum.wrds.n 0
## D.weight.post.stem.sum 0
## D.weight.post.stop.sum 0
## D.weight.sum 0
## max.sold.fctr.predict.Final.Ensemble.glmnet.N
## D.chrs.uppr.n.log 4.465908
## D.ratio.weight.sum.wrds.n 5.241404
## D.weight.post.stem.sum 6.605876
## D.weight.post.stop.sum 6.682933
## D.weight.sum 6.605876
## max.sold.fctr.predict.Final.Ensemble.glmnet.Y
## D.chrs.uppr.n.log 4.430817
## D.ratio.weight.sum.wrds.n 5.241404
## D.weight.post.stem.sum 5.994103
## D.weight.post.stop.sum 6.851448
## D.weight.sum 5.994103
## min.sold.fctr.predict.Final.Ensemble.glmnet.N
## D.chrs.uppr.n.log 0
## D.ratio.weight.sum.wrds.n 0
## D.weight.post.stem.sum 0
## D.weight.post.stop.sum 0
## D.weight.sum 0
## min.sold.fctr.predict.Final.Ensemble.glmnet.Y
## D.chrs.uppr.n.log 0
## D.ratio.weight.sum.wrds.n 0
## D.weight.post.stem.sum 0
## D.weight.post.stop.sum 0
## D.weight.sum 0
## [1] "OOBobs sold.fctr.predict.Ensemble.repeatedcv.glmnet Y: min < min of Train range: 3"
## UniqueID sold.fctr.predict.Ensemble.repeatedcv.glmnet
## 915 10918 Y
## 1594 11603 Y
## 227 10227 Y
## D.ratio.wrds.stop.n.wrds.n D.weight.sum.stem.stop.Ratio
## 915 0.04347826 0.9756676
## 1594 0.40000000 0.7143207
## 227 0.30000000 0.7741412
## id cor.y
## D.ratio.wrds.stop.n.wrds.n D.ratio.wrds.stop.n.wrds.n 0.059547328
## D.weight.sum.stem.stop.Ratio D.weight.sum.stem.stop.Ratio 0.002381527
## exclude.as.feat cor.y.abs
## D.ratio.wrds.stop.n.wrds.n FALSE 0.059547328
## D.weight.sum.stem.stop.Ratio FALSE 0.002381527
## cor.high.X freqRatio
## D.ratio.wrds.stop.n.wrds.n D.terms.post.stop.n.log 18.06780
## D.weight.sum.stem.stop.Ratio <NA> 64.47059
## percentUnique zeroVar nzv is.cor.y.abs.low
## D.ratio.wrds.stop.n.wrds.n 4.27027 FALSE FALSE FALSE
## D.weight.sum.stem.stop.Ratio 33.56757 FALSE FALSE FALSE
## interaction.feat shapiro.test.p.value
## D.ratio.wrds.stop.n.wrds.n <NA> 5.138767e-49
## D.weight.sum.stem.stop.Ratio <NA> 7.686585e-54
## rsp_var_raw id_var rsp_var max min
## D.ratio.wrds.stop.n.wrds.n FALSE NA NA 1 0.04347826
## D.weight.sum.stem.stop.Ratio FALSE NA NA 1 0.71432071
## max.sold.fctr.N max.sold.fctr.Y
## D.ratio.wrds.stop.n.wrds.n 1 1
## D.weight.sum.stem.stop.Ratio 1 1
## min.sold.fctr.N min.sold.fctr.Y
## D.ratio.wrds.stop.n.wrds.n 0.04347826 0.04545455
## D.weight.sum.stem.stop.Ratio 0.76580062 0.81266745
## max.sold.fctr.predict.Ensemble.repeatedcv.glmnet.N
## D.ratio.wrds.stop.n.wrds.n 1
## D.weight.sum.stem.stop.Ratio 1
## max.sold.fctr.predict.Ensemble.repeatedcv.glmnet.Y
## D.ratio.wrds.stop.n.wrds.n 1
## D.weight.sum.stem.stop.Ratio 1
## min.sold.fctr.predict.Ensemble.repeatedcv.glmnet.N
## D.ratio.wrds.stop.n.wrds.n 0.04761905
## D.weight.sum.stem.stop.Ratio 0.80128574
## min.sold.fctr.predict.Ensemble.repeatedcv.glmnet.Y
## D.ratio.wrds.stop.n.wrds.n 0.04347826
## D.weight.sum.stem.stop.Ratio 0.71432071
## max.sold.fctr.predict.Final.Ensemble.glmnet.N
## D.ratio.wrds.stop.n.wrds.n 1
## D.weight.sum.stem.stop.Ratio 1
## max.sold.fctr.predict.Final.Ensemble.glmnet.Y
## D.ratio.wrds.stop.n.wrds.n 1
## D.weight.sum.stem.stop.Ratio 1
## min.sold.fctr.predict.Final.Ensemble.glmnet.N
## D.ratio.wrds.stop.n.wrds.n 0.0500000
## D.weight.sum.stem.stop.Ratio 0.8462026
## min.sold.fctr.predict.Final.Ensemble.glmnet.Y
## D.ratio.wrds.stop.n.wrds.n 0.0500000
## D.weight.sum.stem.stop.Ratio 0.7284632
## [1] "OOBobs sold.fctr.predict.Ensemble.repeatedcv.glmnet Y: max > max of Train range: 6"
## UniqueID sold.fctr.predict.Ensemble.repeatedcv.glmnet
## 873 10876 Y
## 1612 11621 Y
## 915 10918 Y
## 1594 11603 Y
## 119 10119 Y
## 812 10814 Y
## D.terms.post.stem.n.log D.terms.post.stop.n.log
## 873 1.3862944 1.3862944
## 1612 2.3025851 2.3025851
## 915 3.0445224 3.0445224
## 1594 1.3862944 1.3862944
## 119 1.6094379 1.6094379
## 812 0.6931472 0.6931472
## D.weight.post.stem.sum D.weight.post.stop.sum D.weight.sum
## 873 7.258891 7.484915 7.258891
## 1612 4.333960 4.498634 4.333960
## 915 4.623058 4.738354 4.623058
## 1594 5.066153 7.092267 5.066153
## 119 7.332280 7.332280 7.332280
## 812 8.048759 8.563332 8.048759
## D.wrds.stop.n.log D.wrds.unq.n.log
## 873 0.6931472 1.3862944
## 1612 2.3025851 2.3025851
## 915 0.0000000 3.0445224
## 1594 0.6931472 1.3862944
## 119 0.6931472 1.6094379
## 812 1.0986123 0.6931472
## id cor.y
## D.terms.post.stem.n.log D.terms.post.stem.n.log -0.062220631
## D.terms.post.stop.n.log D.terms.post.stop.n.log -0.062526435
## D.weight.post.stem.sum D.weight.post.stem.sum -0.047105442
## D.weight.post.stop.sum D.weight.post.stop.sum -0.045009208
## D.weight.sum D.weight.sum -0.047105442
## D.wrds.stop.n.log D.wrds.stop.n.log 0.003851183
## D.wrds.unq.n.log D.wrds.unq.n.log -0.062220631
## exclude.as.feat cor.y.abs
## D.terms.post.stem.n.log FALSE 0.062220631
## D.terms.post.stop.n.log FALSE 0.062526435
## D.weight.post.stem.sum FALSE 0.047105442
## D.weight.post.stop.sum FALSE 0.045009208
## D.weight.sum FALSE 0.047105442
## D.wrds.stop.n.log FALSE 0.003851183
## D.wrds.unq.n.log FALSE 0.062220631
## cor.high.X freqRatio percentUnique
## D.terms.post.stem.n.log D.terms.post.stop.n.log 11.714286 1.1351351
## D.terms.post.stop.n.log <NA> 13.325000 1.1351351
## D.weight.post.stem.sum D.terms.post.stop.n.log 62.705882 34.6486486
## D.weight.post.stop.sum D.weight.post.stem.sum 62.705882 34.7567568
## D.weight.sum D.weight.post.stem.sum 62.705882 34.6486486
## D.wrds.stop.n.log <NA> 8.732484 0.5405405
## D.wrds.unq.n.log D.terms.post.stem.n.log 11.714286 1.1351351
## zeroVar nzv is.cor.y.abs.low interaction.feat
## D.terms.post.stem.n.log FALSE FALSE FALSE <NA>
## D.terms.post.stop.n.log FALSE FALSE FALSE <NA>
## D.weight.post.stem.sum FALSE FALSE FALSE <NA>
## D.weight.post.stop.sum FALSE FALSE FALSE <NA>
## D.weight.sum FALSE FALSE FALSE <NA>
## D.wrds.stop.n.log FALSE FALSE FALSE <NA>
## D.wrds.unq.n.log FALSE FALSE FALSE <NA>
## shapiro.test.p.value rsp_var_raw id_var rsp_var
## D.terms.post.stem.n.log 1.386439e-48 FALSE NA NA
## D.terms.post.stop.n.log 1.391188e-48 FALSE NA NA
## D.weight.post.stem.sum 2.197748e-48 FALSE NA NA
## D.weight.post.stop.sum 2.648991e-48 FALSE NA NA
## D.weight.sum 2.197748e-48 FALSE NA NA
## D.wrds.stop.n.log 4.444633e-54 FALSE NA NA
## D.wrds.unq.n.log 1.386439e-48 FALSE NA NA
## max min max.sold.fctr.N max.sold.fctr.Y
## D.terms.post.stem.n.log 3.044522 0 2.944439 2.995732
## D.terms.post.stop.n.log 3.044522 0 2.995732 2.995732
## D.weight.post.stem.sum 8.048759 0 6.702992 7.048759
## D.weight.post.stop.sum 8.563332 0 7.202321 7.048759
## D.weight.sum 8.048759 0 6.702992 7.048759
## D.wrds.stop.n.log 2.564949 0 2.302585 2.197225
## D.wrds.unq.n.log 3.044522 0 2.944439 2.995732
## min.sold.fctr.N min.sold.fctr.Y
## D.terms.post.stem.n.log 0 0
## D.terms.post.stop.n.log 0 0
## D.weight.post.stem.sum 0 0
## D.weight.post.stop.sum 0 0
## D.weight.sum 0 0
## D.wrds.stop.n.log 0 0
## D.wrds.unq.n.log 0 0
## max.sold.fctr.predict.Ensemble.repeatedcv.glmnet.N
## D.terms.post.stem.n.log 2.944439
## D.terms.post.stop.n.log 2.995732
## D.weight.post.stem.sum 7.257702
## D.weight.post.stop.sum 7.278648
## D.weight.sum 7.257702
## D.wrds.stop.n.log 2.302585
## D.wrds.unq.n.log 2.944439
## max.sold.fctr.predict.Ensemble.repeatedcv.glmnet.Y
## D.terms.post.stem.n.log 3.044522
## D.terms.post.stop.n.log 3.044522
## D.weight.post.stem.sum 8.048759
## D.weight.post.stop.sum 8.563332
## D.weight.sum 8.048759
## D.wrds.stop.n.log 2.302585
## D.wrds.unq.n.log 3.044522
## min.sold.fctr.predict.Ensemble.repeatedcv.glmnet.N
## D.terms.post.stem.n.log 0
## D.terms.post.stop.n.log 0
## D.weight.post.stem.sum 0
## D.weight.post.stop.sum 0
## D.weight.sum 0
## D.wrds.stop.n.log 0
## D.wrds.unq.n.log 0
## min.sold.fctr.predict.Ensemble.repeatedcv.glmnet.Y
## D.terms.post.stem.n.log 0
## D.terms.post.stop.n.log 0
## D.weight.post.stem.sum 0
## D.weight.post.stop.sum 0
## D.weight.sum 0
## D.wrds.stop.n.log 0
## D.wrds.unq.n.log 0
## max.sold.fctr.predict.Final.Ensemble.glmnet.N
## D.terms.post.stem.n.log 2.944439
## D.terms.post.stop.n.log 2.944439
## D.weight.post.stem.sum 6.605876
## D.weight.post.stop.sum 6.682933
## D.weight.sum 6.605876
## D.wrds.stop.n.log 2.397895
## D.wrds.unq.n.log 2.944439
## max.sold.fctr.predict.Final.Ensemble.glmnet.Y
## D.terms.post.stem.n.log 2.944439
## D.terms.post.stop.n.log 2.944439
## D.weight.post.stem.sum 5.994103
## D.weight.post.stop.sum 6.851448
## D.weight.sum 5.994103
## D.wrds.stop.n.log 2.564949
## D.wrds.unq.n.log 2.944439
## min.sold.fctr.predict.Final.Ensemble.glmnet.N
## D.terms.post.stem.n.log 0
## D.terms.post.stop.n.log 0
## D.weight.post.stem.sum 0
## D.weight.post.stop.sum 0
## D.weight.sum 0
## D.wrds.stop.n.log 0
## D.wrds.unq.n.log 0
## min.sold.fctr.predict.Final.Ensemble.glmnet.Y
## D.terms.post.stem.n.log 0
## D.terms.post.stop.n.log 0
## D.weight.post.stem.sum 0
## D.weight.post.stop.sum 0
## D.weight.sum 0
## D.wrds.stop.n.log 0
## D.wrds.unq.n.log 0
## [1] "OOBobs total range outliers: 12"
## [1] "newobs sold.fctr.predict.Final.Ensemble.glmnet N: min < min of Train range: 1"
## UniqueID sold.fctr.predict.Final.Ensemble.glmnet sprice.d20nexp
## 2614 12625 N 1.929714e-22
## id cor.y exclude.as.feat cor.y.abs
## sprice.d20nexp sprice.d20nexp 0.3979951 FALSE 0.3979951
## cor.high.X freqRatio percentUnique zeroVar nzv
## sprice.d20nexp sprice.log10 2.807692 30.21622 FALSE FALSE
## is.cor.y.abs.low interaction.feat shapiro.test.p.value
## sprice.d20nexp FALSE <NA> 1.494597e-59
## rsp_var_raw id_var rsp_var max min
## sprice.d20nexp FALSE NA NA 0.9995001 1.929714e-22
## max.sold.fctr.N max.sold.fctr.Y min.sold.fctr.N
## sprice.d20nexp 0.9517052 0.9995001 2.027639e-22
## min.sold.fctr.Y
## sprice.d20nexp 2.200702e-15
## max.sold.fctr.predict.Ensemble.repeatedcv.glmnet.N
## sprice.d20nexp 0.8611384
## max.sold.fctr.predict.Ensemble.repeatedcv.glmnet.Y
## sprice.d20nexp 0.9995001
## min.sold.fctr.predict.Ensemble.repeatedcv.glmnet.N
## sprice.d20nexp 9.484101e-19
## min.sold.fctr.predict.Ensemble.repeatedcv.glmnet.Y
## sprice.d20nexp 2.087968e-14
## max.sold.fctr.predict.Final.Ensemble.glmnet.N
## sprice.d20nexp 0.6706553
## max.sold.fctr.predict.Final.Ensemble.glmnet.Y
## sprice.d20nexp 0.9995001
## min.sold.fctr.predict.Final.Ensemble.glmnet.N
## sprice.d20nexp 1.929714e-22
## min.sold.fctr.predict.Final.Ensemble.glmnet.Y
## sprice.d20nexp 2.681004e-14
## [1] "newobs sold.fctr.predict.Final.Ensemble.glmnet N: max > max of Train range: 2"
## UniqueID sold.fctr.predict.Final.Ensemble.glmnet D.chrs.pnct13.n.log
## 2048 12059 N 2.708050
## 2614 12625 N 1.098612
## D.wrds.stop.n.log sprice.log10 sprice.root2
## 2048 0.000000 4.941642 11.83216
## 2614 2.397895 6.907745 31.62262
## id cor.y exclude.as.feat
## D.chrs.pnct13.n.log D.chrs.pnct13.n.log -0.032095777 FALSE
## D.wrds.stop.n.log D.wrds.stop.n.log 0.003851183 FALSE
## sprice.log10 sprice.log10 -0.469398937 FALSE
## sprice.root2 sprice.root2 -0.511275385 FALSE
## cor.y.abs cor.high.X freqRatio
## D.chrs.pnct13.n.log 0.032095777 D.terms.post.stop.n.log 5.272374
## D.wrds.stop.n.log 0.003851183 <NA> 8.732484
## sprice.log10 0.469398937 sprice.root2 2.807692
## sprice.root2 0.511275385 <NA> 2.807692
## percentUnique zeroVar nzv is.cor.y.abs.low
## D.chrs.pnct13.n.log 0.4864865 FALSE FALSE FALSE
## D.wrds.stop.n.log 0.5405405 FALSE FALSE FALSE
## sprice.log10 30.2162162 FALSE FALSE FALSE
## sprice.root2 30.2162162 FALSE FALSE FALSE
## interaction.feat shapiro.test.p.value rsp_var_raw
## D.chrs.pnct13.n.log <NA> 1.157759e-53 FALSE
## D.wrds.stop.n.log <NA> 4.444633e-54 FALSE
## sprice.log10 <NA> 1.697609e-47 FALSE
## sprice.root2 <NA> 6.339275e-16 FALSE
## id_var rsp_var max min max.sold.fctr.N
## D.chrs.pnct13.n.log NA NA 2.708050 0.00000 1.945910
## D.wrds.stop.n.log NA NA 2.564949 0.00000 2.302585
## sprice.log10 NA NA 6.907745 -4.60517 6.906755
## sprice.root2 NA NA 31.622618 0.10000 31.606961
## max.sold.fctr.Y min.sold.fctr.N min.sold.fctr.Y
## D.chrs.pnct13.n.log 2.197225 0.00000000 0.00000
## D.wrds.stop.n.log 2.302585 0.00000000 0.00000
## sprice.log10 6.514713 -0.01005034 -4.60517
## sprice.root2 25.980762 0.99498744 0.10000
## max.sold.fctr.predict.Ensemble.repeatedcv.glmnet.N
## D.chrs.pnct13.n.log 1.609438
## D.wrds.stop.n.log 2.302585
## sprice.log10 6.721414
## sprice.root2 28.809547
## max.sold.fctr.predict.Ensemble.repeatedcv.glmnet.Y
## D.chrs.pnct13.n.log 1.791759
## D.wrds.stop.n.log 2.302585
## sprice.log10 6.445720
## sprice.root2 25.099801
## min.sold.fctr.predict.Ensemble.repeatedcv.glmnet.N
## D.chrs.pnct13.n.log 0.000000
## D.wrds.stop.n.log 0.000000
## sprice.log10 1.095273
## sprice.root2 1.729162
## min.sold.fctr.predict.Ensemble.repeatedcv.glmnet.Y
## D.chrs.pnct13.n.log 0.00000
## D.wrds.stop.n.log 0.00000
## sprice.log10 -4.60517
## sprice.root2 0.10000
## max.sold.fctr.predict.Final.Ensemble.glmnet.N
## D.chrs.pnct13.n.log 2.708050
## D.wrds.stop.n.log 2.397895
## sprice.log10 6.907745
## sprice.root2 31.622618
## max.sold.fctr.predict.Final.Ensemble.glmnet.Y
## D.chrs.pnct13.n.log 1.791759
## D.wrds.stop.n.log 2.564949
## sprice.log10 6.437752
## sprice.root2 25.000000
## min.sold.fctr.predict.Final.Ensemble.glmnet.N
## D.chrs.pnct13.n.log 0.000000
## D.wrds.stop.n.log 0.000000
## sprice.log10 2.078191
## sprice.root2 2.826659
## min.sold.fctr.predict.Final.Ensemble.glmnet.Y
## D.chrs.pnct13.n.log 0.00000
## D.wrds.stop.n.log 0.00000
## sprice.log10 -4.60517
## sprice.root2 0.10000
## [1] "newobs sold.fctr.predict.Final.Ensemble.glmnet Y: max > max of Train range: 3"
## UniqueID sold.fctr.predict.Final.Ensemble.glmnet D.chrs.n.log
## 2020 12031 Y 4.663439
## 2076 12087 Y 4.663439
## 2329 12340 Y 4.624973
## D.wrds.stop.n.log
## 2020 1.386294
## 2076 1.098612
## 2329 2.564949
## id cor.y exclude.as.feat
## D.chrs.n.log D.chrs.n.log -0.055576665 FALSE
## D.wrds.stop.n.log D.wrds.stop.n.log 0.003851183 FALSE
## cor.y.abs cor.high.X freqRatio
## D.chrs.n.log 0.055576665 D.terms.post.stop.n.log 13.455696
## D.wrds.stop.n.log 0.003851183 <NA> 8.732484
## percentUnique zeroVar nzv is.cor.y.abs.low
## D.chrs.n.log 5.3513514 FALSE FALSE FALSE
## D.wrds.stop.n.log 0.5405405 FALSE FALSE FALSE
## interaction.feat shapiro.test.p.value rsp_var_raw id_var
## D.chrs.n.log <NA> 4.162678e-50 FALSE NA
## D.wrds.stop.n.log <NA> 4.444633e-54 FALSE NA
## rsp_var max min max.sold.fctr.N max.sold.fctr.Y
## D.chrs.n.log NA 4.682131 0 4.682131 4.644391
## D.wrds.stop.n.log NA 2.564949 0 2.302585 2.302585
## min.sold.fctr.N min.sold.fctr.Y
## D.chrs.n.log 0 0
## D.wrds.stop.n.log 0 0
## max.sold.fctr.predict.Ensemble.repeatedcv.glmnet.N
## D.chrs.n.log 4.644391
## D.wrds.stop.n.log 2.302585
## max.sold.fctr.predict.Ensemble.repeatedcv.glmnet.Y
## D.chrs.n.log 4.624973
## D.wrds.stop.n.log 2.302585
## min.sold.fctr.predict.Ensemble.repeatedcv.glmnet.N
## D.chrs.n.log 0
## D.wrds.stop.n.log 0
## min.sold.fctr.predict.Ensemble.repeatedcv.glmnet.Y
## D.chrs.n.log 0
## D.wrds.stop.n.log 0
## max.sold.fctr.predict.Final.Ensemble.glmnet.N
## D.chrs.n.log 4.663439
## D.wrds.stop.n.log 2.397895
## max.sold.fctr.predict.Final.Ensemble.glmnet.Y
## D.chrs.n.log 4.663439
## D.wrds.stop.n.log 2.564949
## min.sold.fctr.predict.Final.Ensemble.glmnet.N
## D.chrs.n.log 0
## D.wrds.stop.n.log 0
## min.sold.fctr.predict.Final.Ensemble.glmnet.Y
## D.chrs.n.log 0
## D.wrds.stop.n.log 0
## [1] "newobs total range outliers: 5"#stop(here"); glb_to_sav(); sav_obsout_df <- obsout_df; all.equal(sav_obsout_df, obsout_df); obsout_df <- sav_obsout_df
if (!is.null(glbOutStackFnames)) {
for (fname in glbOutStackFnames) {
print(sprintf("Stacking file %s to prediction output...", fname))
#obsout_df <- dplyr::arrange_(rbind(obsout_df, read.csv(fname)), "UniqueID")
obsout_df <- dplyr::arrange_(rbind(obsout_df,
#read.csv(fname) %>% filter(!(UniqueID %in% obsout_df$UniqueID))),
#read.csv(fname) %>% filter(!(UniqueID %in% obsout_df[, glb_id_var]))),
read.csv(fname) %>%
dplyr::filter_(interp(~!(var %in% obsout_df$var),
var = as.name(glb_id_var)))),
glb_id_var)
if (nrow(obsout_df) != length(unique(obsout_df[, glb_id_var])))
stop("Potential dups in stacked prediction output")
}
}
out_fname <- paste0(glb_out_pfx, "out.csv")
write.csv(obsout_df, out_fname, quote = FALSE, row.names = FALSE)
#cat(" ", "\n", file=submit_fn, append=TRUE)
# print(orderBy(~ -max.auc.OOB, glb_models_df[, c("id",
# "max.auc.OOB", "max.Accuracy.OOB")]))
for (txt_var in glbFeatsText) {
# Print post-stem-words but need post-stop-words for debugging ?
print(sprintf(" All post-stem-words term weights for %s:", txt_var))
myprint_df(glb_post_stem_words_terms_df_lst[[txt_var]])
terms_mtrx <- glb_post_stem_words_terms_mtrx_lst[[txt_var]]
print(glb_allobs_df[
which(terms_mtrx[, tail(glb_post_stem_words_terms_df_lst[[txt_var]], 1)$pos] > 0),
c(glb_id_var, glbFeatsText)])
print(nrow(subset(glb_post_stem_words_terms_df_lst[[txt_var]], freq == 1)))
#print(glb_allobs_df[which(terms_mtrx[, 207] > 0), c(glb_id_var, glbFeatsText)])
#unlist(strsplit(glb_allobs_df[2157, "description"], ""))
#glb_allobs_df[2442, c(glb_id_var, glbFeatsText)]
#terms_mtrx[2442, terms_mtrx[2442, ] > 0]
print(sprintf(" All post-stem-words term freq distribution for %s:", txt_var))
print(table(glb_post_stem_words_terms_df_lst[[txt_var]]$freq))
print(sprintf(" All post-stem-words term length distribution for %s:", txt_var))
print(table(nchar(glb_post_stem_words_terms_df_lst[[txt_var]]$term)))
print(subset(glb_post_stem_words_terms_df_lst[[txt_var]], nchar(term) >= 10))
print(sprintf(" Analyzed term weights for %s:", txt_var))
tmp_df <- glb_post_stem_words_terms_df_lst[[txt_var]]
anl_terms_vctr <- union(select_terms, assoc_terms)
print(subset(tmp_df, term %in% anl_terms_vctr))
# tmp_freq1_df <- subset(tmp_df, freq == 1)
# tmp_freq1_df$top_n <- grepl(paste0(top_n_vctr, collapse="|"), tmp_freq1_df$term)
# print(subset(tmp_freq1_df, top_n == TRUE))
}## [1] " All post-stem-words term weights for descr.my:"
## term weight freq pos cor.y cor.y.abs chisq.stat
## condit condit 160.55353 492 52 -0.033248924 0.033248924 68.76178
## likenew likenew 123.78010 70 117 -0.043168766 0.043168766 22.07653
## use use 111.06226 285 210 0.003025811 0.003025811 45.28121
## in in 102.43546 432 98 -0.071653919 0.071653919 40.30764
## and and 92.37461 331 20 0.006010357 0.006010357 38.45444
## is is 87.72451 335 110 -0.042931491 0.042931491 26.34374
## chisq.pval nzv.freqRatio nzv.percentUnique nzv weight.N
## condit 3.361980e-06 36.82927 1.351351 TRUE 61.51671
## likenew 1.817939e-01 257.71429 0.972973 TRUE 54.11495
## use 1.518411e-02 86.63158 1.513514 TRUE 40.77891
## in 1.981535e-02 33.52174 1.351351 TRUE 46.12932
## and 7.095553e-02 55.24138 1.513514 TRUE 35.73653
## is 5.541564e-01 54.06667 1.567568 TRUE 35.26992
## weight.Y weight.NA
## condit 43.14002 55.89681
## likenew 19.52427 50.14088
## use 35.93377 34.34958
## in 26.82731 29.47884
## and 31.78712 24.85096
## is 23.45595 28.99864
## term weight freq pos cor.y cor.y.abs chisq.stat
## icloud icloud 40.463122 58 96 0.007542854 0.007542854 13.581530
## open open 30.322386 43 143 -0.018325646 0.018325646 11.069771
## protector protector 13.651955 26 161 0.019364354 0.019364354 12.681236
## littl littl 8.766770 13 119 -0.034837176 0.034837176 7.489556
## gentl gentl 8.170129 10 84 0.002810507 0.002810507 3.387204
## had had 7.338423 16 90 -0.023196589 0.023196589 6.352060
## chisq.pval nzv.freqRatio nzv.percentUnique nzv weight.N
## icloud 0.4039601 302.3333 0.7567568 TRUE 12.401881
## open 0.6805464 227.3750 0.8108108 TRUE 13.007680
## protector 0.3926291 609.6667 0.7027027 TRUE 4.853939
## littl 0.2779330 614.0000 0.3783784 TRUE 4.673587
## gentl 0.4952351 614.6667 0.2702703 TRUE 2.044129
## had 0.4992942 613.0000 0.4324324 TRUE 3.551184
## weight.Y weight.NA
## icloud 12.399126 15.662115
## open 7.856977 9.457729
## protector 6.099885 2.698131
## littl 1.016144 3.077039
## gentl 1.956296 4.169704
## had 1.585668 2.201571
## term weight freq pos cor.y cor.y.abs chisq.stat
## 4g 4g 2.200200 3 9 0.0002965679 0.0002965679 2.023052e+00
## sprint sprint 2.037099 2 191 -0.0215577316 0.0215577316 1.281078e-27
## verizon verizon 2.037099 2 212 -0.0215577316 0.0215577316 1.281078e-27
## tmobil tmobil 1.893062 2 203 0.0094480109 0.0094480109 2.023052e+00
## ipad1 ipad1 1.834814 2 101 0.0067428569 0.0067428569 2.023052e+00
## gold gold 1.137069 1 86 -0.0215577316 0.0215577316 1.281078e-27
## chisq.pval nzv.freqRatio nzv.percentUnique nzv weight.N
## 4g 0.3636637 1848 0.1621622 TRUE 0.6989804
## sprint 1.0000000 1849 0.1081081 TRUE 0.7407634
## verizon 1.0000000 1849 0.1081081 TRUE 0.7407634
## tmobil 0.3636637 1848 0.1621622 TRUE 0.7407634
## ipad1 0.3636637 1848 0.1621622 TRUE 0.7977452
## gold 1.0000000 1849 0.1081081 TRUE 1.1370687
## weight.Y weight.NA
## 4g 0.6116078 0.8896114
## sprint 0.0000000 1.2963359
## verizon 0.0000000 1.2963359
## tmobil 1.1522986 0.0000000
## ipad1 1.0370687 0.0000000
## gold 0.0000000 0.0000000
## UniqueID
## 1177 11183
## descr.my
## 1177 *** Likenew, Apple iPadAir2 Gold 128GB. Comes with AC adapter and lightning cable.
## [1] 1
## [1] " All post-stem-words term freq distribution for descr.my:"
##
## 1 2 3 4 5 6 8 9 10 11 12 13 14 15 16 17 18 19
## 1 4 3 3 1 2 1 1 13 8 13 6 13 5 9 5 6 8
## 20 21 22 23 24 25 26 27 28 30 31 32 34 35 36 37 38 40
## 2 5 1 3 1 2 5 8 3 1 7 2 3 1 1 3 2 2
## 42 43 44 45 46 50 51 52 53 54 55 57 58 59 61 63 64 65
## 1 2 1 2 2 1 1 2 1 1 1 2 3 1 1 1 1 1
## 67 70 72 75 77 78 79 80 81 82 88 97 99 100 101 106 109 117
## 1 1 2 2 3 1 1 1 1 2 1 1 1 1 1 1 2 2
## 125 129 151 154 160 167 176 197 198 208 211 214 249 279 285 286 331 335
## 2 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1
## 432 492
## 1 1
## [1] " All post-stem-words term length distribution for descr.my:"
##
## 1 2 3 4 5 6 7 8 9 10
## 6 17 28 64 51 29 15 8 6 2
## term weight freq pos cor.y cor.y.abs chisq.stat
## profession profession 9.495620 16 160 -0.07142724 0.07142724 9.508800
## manufactur manufactur 8.616795 8 123 0.01332077 0.01332077 6.928535
## chisq.pval nzv.freqRatio nzv.percentUnique nzv weight.N
## profession 0.04956674 613.0 0.2702703 TRUE 6.606743
## manufactur 0.32750691 921.5 0.3783784 TRUE 3.089182
## weight.Y weight.NA
## profession 0.000000 2.888877
## manufactur 4.481277 1.046336
## [1] " Analyzed term weights for descr.my:"
## term weight freq pos cor.y cor.y.abs chisq.stat
## condit condit 160.55353 492 52 -0.033248924 0.033248924 68.76178
## use use 111.06226 285 210 0.003025811 0.003025811 45.28121
## in in 102.43546 432 98 -0.071653919 0.071653919 40.30764
## good good 86.95851 197 87 0.016394675 0.016394675 33.86257
## screen screen 73.07057 208 172 0.040605274 0.040605274 32.79902
## with with 63.69164 214 223 -0.066087601 0.066087601 33.25670
## of of 52.99927 151 139 0.052259530 0.052259530 34.49697
## mint mint 52.07871 63 128 -0.055691207 0.055691207 32.30119
## or or 51.11272 125 144 -0.022614420 0.022614420 36.49790
## cosmet cosmet 43.18436 117 55 -0.088480863 0.088480863 32.40489
## minor minor 41.27081 117 127 -0.042692360 0.042692360 33.12974
## light light 36.41889 81 116 -0.032814746 0.032814746 35.12786
## 100 100 27.52852 64 3 -0.111398502 0.111398502 30.45739
## from from 24.73125 57 78 -0.030653359 0.030653359 34.28905
## hous hous 24.59165 72 94 -0.129274878 0.129274878 41.95872
## chisq.pval nzv.freqRatio nzv.percentUnique nzv weight.N
## condit 3.361980e-06 36.82927 1.3513514 TRUE 61.51671
## use 1.518411e-02 86.63158 1.5135135 TRUE 40.77891
## in 1.981535e-02 33.52174 1.3513514 TRUE 46.12932
## good 3.748545e-02 95.16667 1.1891892 TRUE 29.35392
## screen 3.549380e-02 70.91667 1.1351351 TRUE 23.12678
## with 1.553541e-02 46.88889 1.0270270 TRUE 30.89756
## of 2.295297e-02 145.00000 1.1351351 TRUE 16.02233
## mint 2.024848e-02 94.89474 1.0270270 TRUE 24.27170
## or 9.163373e-03 87.95000 1.0810811 TRUE 22.27352
## cosmet 3.507470e-03 88.40000 0.8108108 TRUE 22.73428
## minor 1.085239e-02 92.52632 0.9729730 TRUE 20.58455
## light 3.817238e-03 99.27778 0.9189189 TRUE 17.42219
## 100 1.342479e-03 180.50000 0.6486486 TRUE 18.16396
## from 6.072414e-04 95.31579 0.7027027 TRUE 10.97996
## hous 3.344151e-06 81.68182 0.5405405 TRUE 17.25642
## weight.Y weight.NA
## condit 43.140019 55.896806
## use 35.933771 34.349578
## in 26.827307 29.478838
## good 30.064015 27.540573
## screen 28.028461 21.915331
## with 16.195788 16.598293
## of 21.710580 15.266363
## mint 6.994811 20.812208
## or 15.175342 13.663856
## cosmet 7.055599 13.394481
## minor 11.627271 9.058990
## light 10.171779 8.824924
## 100 1.314682 8.049871
## from 5.861552 7.889737
## hous 1.193859 6.141364if (glb_is_classification && glb_is_binomial)
print(glb_models_df[glb_models_df$id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"])## [1] 0.2print(sprintf("glb_sel_mdl_id: %s", glb_sel_mdl_id))## [1] "glb_sel_mdl_id: Ensemble.repeatedcv.glmnet"print(sprintf("glb_fin_mdl_id: %s", glb_fin_mdl_id))## [1] "glb_fin_mdl_id: Final.Ensemble.glmnet"get_dsp_models_df()## [1] "Cross Validation issues:"## Warning in get_dsp_models_df(): Cross Validation issues:## MFO.myMFO_classfr Random.myrandom_classfr
## 0 0
## Max.cor.Y.rcv.1X1.glmnet Max.cor.Y.rcv.1X1.cp.0.rpart
## 0 0
## CSM.X.bagEarth CSM.X.YeoJohnson.bagEarth
## 1 1
## CSM.X.center.scale.bagEarth CSM.X.spatialSign.bagEarth
## 1 1
## CSM.X.center.scale.Train.bagEarth CSM.X.Train.bagEarth
## 1 1
## CSM.X.YeoJohnson.Train.bagEarth CSM.X.spatialSign.Train.bagEarth
## 1 1## id
## CSM.X.center.scale.bagEarth CSM.X.center.scale.bagEarth
## CSM.X.bagEarth CSM.X.bagEarth
## CSM.X.Interact.glmnet CSM.X.Interact.glmnet
## CSM.X.YeoJohnson.bagEarth CSM.X.YeoJohnson.bagEarth
## CSM.X.earth CSM.X.earth
## CSM.X.spatialSign.bagEarth CSM.X.spatialSign.bagEarth
## CSM.X.nnet CSM.X.nnet
## CSM.X.avNNet CSM.X.avNNet
## RFE.X.glm RFE.X.glm
## CSM.X.glm CSM.X.glm
## CSM.X.glmnet CSM.X.glmnet
## Max.cor.Y.rcv.1X1.glmnet Max.cor.Y.rcv.1X1.glmnet
## CSM.X.gbm CSM.X.gbm
## Ensemble.cv.glmnet Ensemble.cv.glmnet
## Ensemble.LGOCV.glmnet Ensemble.LGOCV.glmnet
## Ensemble.repeatedcv.glmnet Ensemble.repeatedcv.glmnet
## Ensemble.adaptive_cv.glmnet Ensemble.adaptive_cv.glmnet
## Interact.High.cor.Y.glmnet Interact.High.cor.Y.glmnet
## Max.cor.Y.rpart Max.cor.Y.rpart
## Low.cor.X.glmnet Low.cor.X.glmnet
## CSM.X.bayesglm CSM.X.bayesglm
## Max.cor.Y.rcv.3X3.glmnet Max.cor.Y.rcv.3X3.glmnet
## Max.cor.Y.rcv.3X1.glmnet Max.cor.Y.rcv.3X1.glmnet
## Max.cor.Y.rcv.3X5.glmnet Max.cor.Y.rcv.3X5.glmnet
## Ensemble.boot632.glmnet Ensemble.boot632.glmnet
## Ensemble.boot.glmnet Ensemble.boot.glmnet
## Max.cor.Y.rcv.5X1.glmnet Max.cor.Y.rcv.5X1.glmnet
## Max.cor.Y.rcv.5X3.glmnet Max.cor.Y.rcv.5X3.glmnet
## Max.cor.Y.rcv.5X5.glmnet Max.cor.Y.rcv.5X5.glmnet
## RFE.X.glmnet RFE.X.glmnet
## All.X.glmnet All.X.glmnet
## CSM.X.rf CSM.X.rf
## CSM.X.rpart CSM.X.rpart
## Max.cor.Y.rcv.1X1.cp.0.rpart Max.cor.Y.rcv.1X1.cp.0.rpart
## Ensemble.repeatedcv.glm Ensemble.repeatedcv.glm
## MFO.myMFO_classfr MFO.myMFO_classfr
## Random.myrandom_classfr Random.myrandom_classfr
## Final.Ensemble.glm Final.Ensemble.glm
## Final.Ensemble.glmnet Final.Ensemble.glmnet
## CSM.X.Train.avNNet CSM.X.Train.avNNet
## Low.cor.X.Train.glmnet Low.cor.X.Train.glmnet
## CSM.X.Train.earth CSM.X.Train.earth
## CSM.X.Train.nnet CSM.X.Train.nnet
## CSM.X.Interact.Train.glmnet CSM.X.Interact.Train.glmnet
## CSM.X.Train.bayesglm CSM.X.Train.bayesglm
## All.X.Train.glmnet All.X.Train.glmnet
## RFE.X.Train.glmnet RFE.X.Train.glmnet
## CSM.X.Train.gbm CSM.X.Train.gbm
## RFE.X.Train.glm RFE.X.Train.glm
## CSM.X.Train.glm CSM.X.Train.glm
## CSM.X.center.scale.Train.bagEarth CSM.X.center.scale.Train.bagEarth
## CSM.X.Train.bagEarth CSM.X.Train.bagEarth
## CSM.X.YeoJohnson.Train.bagEarth CSM.X.YeoJohnson.Train.bagEarth
## CSM.X.spatialSign.Train.bagEarth CSM.X.spatialSign.Train.bagEarth
## CSM.X.Train.rpart CSM.X.Train.rpart
## Interact.High.cor.Y.Train.glmnet Interact.High.cor.Y.Train.glmnet
## Max.cor.Y.rcv.1X1.Train.glmnet Max.cor.Y.rcv.1X1.Train.glmnet
## Max.cor.Y.rcv.5X5.Train.glmnet Max.cor.Y.rcv.5X5.Train.glmnet
## Max.cor.Y.rcv.5X1.Train.glmnet Max.cor.Y.rcv.5X1.Train.glmnet
## Max.cor.Y.rcv.5X3.Train.glmnet Max.cor.Y.rcv.5X3.Train.glmnet
## Max.cor.Y.rcv.3X5.Train.glmnet Max.cor.Y.rcv.3X5.Train.glmnet
## Max.cor.Y.rcv.3X3.Train.glmnet Max.cor.Y.rcv.3X3.Train.glmnet
## Max.cor.Y.rcv.3X1.Train.glmnet Max.cor.Y.rcv.3X1.Train.glmnet
## Max.cor.Y.rcv.1X1.cp.0.Train.rpart Max.cor.Y.rcv.1X1.cp.0.Train.rpart
## Max.cor.Y.Train.rpart Max.cor.Y.Train.rpart
## max.Accuracy.OOB max.AUCROCR.OOB
## CSM.X.center.scale.bagEarth 0.8108434 0.8764143
## CSM.X.bagEarth 0.8072289 0.8766246
## CSM.X.Interact.glmnet 0.8012048 0.8636807
## CSM.X.YeoJohnson.bagEarth 0.8000000 0.8782951
## CSM.X.earth 0.7987952 0.8533245
## CSM.X.spatialSign.bagEarth 0.7927711 0.8595102
## CSM.X.nnet 0.7915663 0.8693232
## CSM.X.avNNet 0.7891566 0.8614202
## RFE.X.glm 0.7879518 0.8700066
## CSM.X.glm 0.7867470 0.8705557
## CSM.X.glmnet 0.7855422 0.8713734
## Max.cor.Y.rcv.1X1.glmnet 0.7722892 0.8197236
## CSM.X.gbm 0.7698795 0.8482836
## Ensemble.cv.glmnet 0.7698795 0.8382077
## Ensemble.LGOCV.glmnet 0.7698795 0.8382077
## Ensemble.repeatedcv.glmnet 0.7698795 0.8382077
## Ensemble.adaptive_cv.glmnet 0.7698795 0.8382077
## Interact.High.cor.Y.glmnet 0.7698795 0.8189000
## Max.cor.Y.rpart 0.7698795 0.7855854
## Low.cor.X.glmnet 0.7686747 0.8727052
## CSM.X.bayesglm 0.7674699 0.8720510
## Max.cor.Y.rcv.3X3.glmnet 0.7662651 0.8197470
## Max.cor.Y.rcv.3X1.glmnet 0.7662651 0.8197353
## Max.cor.Y.rcv.3X5.glmnet 0.7650602 0.8196593
## Ensemble.boot632.glmnet 0.7638554 0.8366715
## Ensemble.boot.glmnet 0.7638554 0.8366715
## Max.cor.Y.rcv.5X1.glmnet 0.7638554 0.8195600
## Max.cor.Y.rcv.5X3.glmnet 0.7638554 0.8195600
## Max.cor.Y.rcv.5X5.glmnet 0.7638554 0.8193790
## RFE.X.glmnet 0.7626506 0.8728278
## All.X.glmnet 0.7614458 0.8721444
## CSM.X.rf 0.7602410 0.8492707
## CSM.X.rpart 0.7590361 0.7890637
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.7433735 0.7934621
## Ensemble.repeatedcv.glm 0.7421687 0.7426826
## MFO.myMFO_classfr 0.4614458 0.5000000
## Random.myrandom_classfr 0.4614458 0.4956046
## Final.Ensemble.glm NA NA
## Final.Ensemble.glmnet NA NA
## CSM.X.Train.avNNet NA NA
## Low.cor.X.Train.glmnet NA NA
## CSM.X.Train.earth NA NA
## CSM.X.Train.nnet NA NA
## CSM.X.Interact.Train.glmnet NA NA
## CSM.X.Train.bayesglm NA NA
## All.X.Train.glmnet NA NA
## RFE.X.Train.glmnet NA NA
## CSM.X.Train.gbm NA NA
## RFE.X.Train.glm NA NA
## CSM.X.Train.glm NA NA
## CSM.X.center.scale.Train.bagEarth NA NA
## CSM.X.Train.bagEarth NA NA
## CSM.X.YeoJohnson.Train.bagEarth NA NA
## CSM.X.spatialSign.Train.bagEarth NA NA
## CSM.X.Train.rpart NA NA
## Interact.High.cor.Y.Train.glmnet NA NA
## Max.cor.Y.rcv.1X1.Train.glmnet NA NA
## Max.cor.Y.rcv.5X5.Train.glmnet NA NA
## Max.cor.Y.rcv.5X1.Train.glmnet NA NA
## Max.cor.Y.rcv.5X3.Train.glmnet NA NA
## Max.cor.Y.rcv.3X5.Train.glmnet NA NA
## Max.cor.Y.rcv.3X3.Train.glmnet NA NA
## Max.cor.Y.rcv.3X1.Train.glmnet NA NA
## Max.cor.Y.rcv.1X1.cp.0.Train.rpart NA NA
## Max.cor.Y.Train.rpart NA NA
## max.AUCpROC.OOB max.Accuracy.fit
## CSM.X.center.scale.bagEarth 0.8070017 0.7965221
## CSM.X.bagEarth 0.8036460 0.8024729
## CSM.X.Interact.glmnet 0.7820106 0.8316958
## CSM.X.YeoJohnson.bagEarth 0.8060701 0.8010417
## CSM.X.earth 0.8002815 0.8241614
## CSM.X.spatialSign.bagEarth 0.7896595 0.8149705
## CSM.X.nnet 0.7883570 0.8267681
## CSM.X.avNNet 0.7797676 0.8271026
## RFE.X.glm 0.7851882 0.8217402
## CSM.X.glm 0.7836958 0.8264355
## CSM.X.glmnet 0.7937629 0.8323381
## Max.cor.Y.rcv.1X1.glmnet 0.7665376 0.7843137
## CSM.X.gbm 0.7573963 0.8323391
## Ensemble.cv.glmnet 0.7667245 0.9362655
## Ensemble.LGOCV.glmnet 0.7667245 0.9346405
## Ensemble.repeatedcv.glmnet 0.7667245 0.9329967
## Ensemble.adaptive_cv.glmnet 0.7667245 0.9329967
## Interact.High.cor.Y.glmnet 0.7551533 0.8026098
## Max.cor.Y.rpart 0.7445254 0.7964073
## Low.cor.X.glmnet 0.7801532 0.8349657
## CSM.X.bayesglm 0.7902203 0.8293912
## Max.cor.Y.rcv.3X3.glmnet 0.7603840 0.7954221
## Max.cor.Y.rcv.3X1.glmnet 0.7592654 0.7950911
## Max.cor.Y.rcv.3X5.glmnet 0.7603840 0.7962655
## Ensemble.boot632.glmnet 0.7678431 0.9372772
## Ensemble.boot.glmnet 0.7678431 0.9367195
## Max.cor.Y.rcv.5X1.glmnet 0.7615026 0.7990899
## Max.cor.Y.rcv.5X3.glmnet 0.7615026 0.7964364
## Max.cor.Y.rcv.5X5.glmnet 0.7641135 0.7970687
## RFE.X.glmnet 0.7861198 0.8259789
## All.X.glmnet 0.7835089 0.8303886
## CSM.X.rf 0.7831321 0.8310413
## CSM.X.rpart 0.7534652 0.8251457
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.7504950 0.8323529
## Ensemble.repeatedcv.glm 0.7426826 0.6918230
## MFO.myMFO_classfr 0.5000000 0.4627451
## Random.myrandom_classfr 0.5064252 0.4627451
## Final.Ensemble.glm NA 0.8819861
## Final.Ensemble.glmnet NA 0.8805434
## CSM.X.Train.avNNet NA 0.8232434
## Low.cor.X.Train.glmnet NA 0.8227067
## CSM.X.Train.earth NA 0.8223456
## CSM.X.Train.nnet NA 0.8216195
## CSM.X.Interact.Train.glmnet NA 0.8212657
## CSM.X.Train.bayesglm NA 0.8210830
## All.X.Train.glmnet NA 0.8203656
## RFE.X.Train.glmnet NA 0.8203656
## CSM.X.Train.gbm NA 0.8198219
## RFE.X.Train.glm NA 0.8185584
## CSM.X.Train.glm NA 0.8172966
## CSM.X.center.scale.Train.bagEarth NA 0.8108098
## CSM.X.Train.bagEarth NA 0.8108098
## CSM.X.YeoJohnson.Train.bagEarth NA 0.8108098
## CSM.X.spatialSign.Train.bagEarth NA 0.8108098
## CSM.X.Train.rpart NA 0.8030677
## Interact.High.cor.Y.Train.glmnet NA 0.7872117
## Max.cor.Y.rcv.1X1.Train.glmnet NA 0.7854082
## Max.cor.Y.rcv.5X5.Train.glmnet NA 0.7854082
## Max.cor.Y.rcv.5X1.Train.glmnet NA 0.7854082
## Max.cor.Y.rcv.5X3.Train.glmnet NA 0.7854082
## Max.cor.Y.rcv.3X5.Train.glmnet NA 0.7854082
## Max.cor.Y.rcv.3X3.Train.glmnet NA 0.7854082
## Max.cor.Y.rcv.3X1.Train.glmnet NA 0.7854082
## Max.cor.Y.rcv.1X1.cp.0.Train.rpart NA 0.7783853
## Max.cor.Y.Train.rpart NA 0.7783853
## opt.prob.threshold.fit
## CSM.X.center.scale.bagEarth 0.5
## CSM.X.bagEarth 0.5
## CSM.X.Interact.glmnet 0.4
## CSM.X.YeoJohnson.bagEarth 0.5
## CSM.X.earth 0.4
## CSM.X.spatialSign.bagEarth 0.4
## CSM.X.nnet 0.4
## CSM.X.avNNet 0.4
## RFE.X.glm 0.4
## CSM.X.glm 0.4
## CSM.X.glmnet 0.4
## Max.cor.Y.rcv.1X1.glmnet 0.4
## CSM.X.gbm 0.4
## Ensemble.cv.glmnet 0.4
## Ensemble.LGOCV.glmnet 0.4
## Ensemble.repeatedcv.glmnet 0.4
## Ensemble.adaptive_cv.glmnet 0.4
## Interact.High.cor.Y.glmnet 0.4
## Max.cor.Y.rpart 0.5
## Low.cor.X.glmnet 0.5
## CSM.X.bayesglm 0.4
## Max.cor.Y.rcv.3X3.glmnet 0.5
## Max.cor.Y.rcv.3X1.glmnet 0.5
## Max.cor.Y.rcv.3X5.glmnet 0.5
## Ensemble.boot632.glmnet 0.5
## Ensemble.boot.glmnet 0.5
## Max.cor.Y.rcv.5X1.glmnet 0.5
## Max.cor.Y.rcv.5X3.glmnet 0.5
## Max.cor.Y.rcv.5X5.glmnet 0.5
## RFE.X.glmnet 0.4
## All.X.glmnet 0.4
## CSM.X.rf 0.5
## CSM.X.rpart 0.9
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.4
## Ensemble.repeatedcv.glm 0.9
## MFO.myMFO_classfr 0.4
## Random.myrandom_classfr 0.4
## Final.Ensemble.glm 0.3
## Final.Ensemble.glmnet 0.4
## CSM.X.Train.avNNet 0.5
## Low.cor.X.Train.glmnet 0.4
## CSM.X.Train.earth 0.4
## CSM.X.Train.nnet 0.5
## CSM.X.Interact.Train.glmnet 0.4
## CSM.X.Train.bayesglm 0.4
## All.X.Train.glmnet 0.4
## RFE.X.Train.glmnet 0.4
## CSM.X.Train.gbm 0.4
## RFE.X.Train.glm 0.4
## CSM.X.Train.glm 0.4
## CSM.X.center.scale.Train.bagEarth 0.3
## CSM.X.Train.bagEarth 0.3
## CSM.X.YeoJohnson.Train.bagEarth 0.3
## CSM.X.spatialSign.Train.bagEarth 0.3
## CSM.X.Train.rpart 0.5
## Interact.High.cor.Y.Train.glmnet 0.4
## Max.cor.Y.rcv.1X1.Train.glmnet 0.5
## Max.cor.Y.rcv.5X5.Train.glmnet 0.5
## Max.cor.Y.rcv.5X1.Train.glmnet 0.5
## Max.cor.Y.rcv.5X3.Train.glmnet 0.5
## Max.cor.Y.rcv.3X5.Train.glmnet 0.5
## Max.cor.Y.rcv.3X3.Train.glmnet 0.5
## Max.cor.Y.rcv.3X1.Train.glmnet 0.5
## Max.cor.Y.rcv.1X1.cp.0.Train.rpart 0.5
## Max.cor.Y.Train.rpart 0.5
## opt.prob.threshold.OOB
## CSM.X.center.scale.bagEarth 0.5
## CSM.X.bagEarth 0.5
## CSM.X.Interact.glmnet 0.4
## CSM.X.YeoJohnson.bagEarth 0.4
## CSM.X.earth 0.4
## CSM.X.spatialSign.bagEarth 0.4
## CSM.X.nnet 0.5
## CSM.X.avNNet 0.3
## RFE.X.glm 0.5
## CSM.X.glm 0.5
## CSM.X.glmnet 0.4
## Max.cor.Y.rcv.1X1.glmnet 0.4
## CSM.X.gbm 0.3
## Ensemble.cv.glmnet 0.2
## Ensemble.LGOCV.glmnet 0.2
## Ensemble.repeatedcv.glmnet 0.2
## Ensemble.adaptive_cv.glmnet 0.2
## Interact.High.cor.Y.glmnet 0.3
## Max.cor.Y.rpart 0.3
## Low.cor.X.glmnet 0.3
## CSM.X.bayesglm 0.3
## Max.cor.Y.rcv.3X3.glmnet 0.4
## Max.cor.Y.rcv.3X1.glmnet 0.4
## Max.cor.Y.rcv.3X5.glmnet 0.4
## Ensemble.boot632.glmnet 0.2
## Ensemble.boot.glmnet 0.2
## Max.cor.Y.rcv.5X1.glmnet 0.4
## Max.cor.Y.rcv.5X3.glmnet 0.4
## Max.cor.Y.rcv.5X5.glmnet 0.4
## RFE.X.glmnet 0.3
## All.X.glmnet 0.3
## CSM.X.rf 0.3
## CSM.X.rpart 0.3
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.3
## Ensemble.repeatedcv.glm 0.9
## MFO.myMFO_classfr 0.4
## Random.myrandom_classfr 0.4
## Final.Ensemble.glm NA
## Final.Ensemble.glmnet NA
## CSM.X.Train.avNNet NA
## Low.cor.X.Train.glmnet NA
## CSM.X.Train.earth NA
## CSM.X.Train.nnet NA
## CSM.X.Interact.Train.glmnet NA
## CSM.X.Train.bayesglm NA
## All.X.Train.glmnet NA
## RFE.X.Train.glmnet NA
## CSM.X.Train.gbm NA
## RFE.X.Train.glm NA
## CSM.X.Train.glm NA
## CSM.X.center.scale.Train.bagEarth NA
## CSM.X.Train.bagEarth NA
## CSM.X.YeoJohnson.Train.bagEarth NA
## CSM.X.spatialSign.Train.bagEarth NA
## CSM.X.Train.rpart NA
## Interact.High.cor.Y.Train.glmnet NA
## Max.cor.Y.rcv.1X1.Train.glmnet NA
## Max.cor.Y.rcv.5X5.Train.glmnet NA
## Max.cor.Y.rcv.5X1.Train.glmnet NA
## Max.cor.Y.rcv.5X3.Train.glmnet NA
## Max.cor.Y.rcv.3X5.Train.glmnet NA
## Max.cor.Y.rcv.3X3.Train.glmnet NA
## Max.cor.Y.rcv.3X1.Train.glmnet NA
## Max.cor.Y.rcv.1X1.cp.0.Train.rpart NA
## Max.cor.Y.Train.rpart NAif (glb_is_regression) {
print(sprintf("%s OOB RMSE: %0.4f", glb_sel_mdl_id,
glb_models_df[glb_models_df$id == glb_sel_mdl_id, "min.RMSE.OOB"]))
if (!is.null(glb_category_var)) {
#stop(here"); glb_to_sav(); glb_ctgry_df <- sav_ctgry_df
# OOB_ctgry_df <- myget_category_stats(glb_OOBobs_df, glb_sel_mdl_id, "OOB")
# glb_ctgry_df <- merge(glb_ctgry_df, subset(OOB_ctgry_df, select=-.n.OOB),
# by=glb_category_var, all=TRUE)
#
# #glb_fitobs_df <- glb_get_predictions(glb_fitobs_df, glb_sel_mdl_id, glb_rsp_var_out)
# glb_ctgry_df <- merge(glb_ctgry_df,
# myget_category_stats(obs_df=glb_fitobs_df, mdl_id=glb_sel_mdl_id, label="fit"),
# by=glb_category_var, all=TRUE)
# row.names(glb_ctgry_df) <- glb_ctgry_df[, glb_category_var]
glb_ctgry_df <- merge(glb_ctgry_df,
myget_category_stats(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id, label="trn"),
by=glb_category_var, all=TRUE)
row.names(glb_ctgry_df) <- glb_ctgry_df[, glb_category_var]
glb_ctgry_df <- merge(glb_ctgry_df,
myget_category_stats(obs_df=glb_newobs_df, mdl_id=glb_fin_mdl_id, label="new"),
by=glb_category_var, all=TRUE)
row.names(glb_ctgry_df) <- glb_ctgry_df[, glb_category_var]
if (any(grepl("OOB", glbMdlMetricsEval)))
print(orderBy(~-err.abs.OOB.mean, glb_ctgry_df)) else
print(orderBy(~-err.abs.fit.mean, glb_ctgry_df))
print(colSums(glb_ctgry_df[, -grep(glb_category_var, names(glb_ctgry_df))]))
}
if ((glb_rsp_var %in% names(glb_newobs_df)) &&
!(any(is.na(glb_newobs_df[, glb_rsp_var])))) {
pred_stats_df <-
mypredict_mdl(mdl=glb_models_lst[[glb_fin_mdl_id]],
df=glb_newobs_df,
rsp_var=glb_rsp_var,
rsp_var_out=glb_rsp_var_out,
mdl_id=glb_fin_mdl_id,
label="new",
model_summaryFunction=glb_sel_mdl$control$summaryFunction,
model_metric=glb_sel_mdl$metric,
model_metric_maximize=glb_sel_mdl$maximize,
ret_type="stats")
print(sprintf("%s prediction stats for glb_newobs_df:", glb_fin_mdl_id))
print(pred_stats_df)
}
}
if (glb_is_classification) {
print(sprintf("%s OOB confusion matrix & accuracy: ", glb_sel_mdl_id))
print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)],
glb_OOBobs_df[, glb_rsp_var])$table))
if (!is.null(glb_category_var)) {
glb_ctgry_df <- merge(glb_ctgry_df,
myget_category_stats(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id, label="trn"),
by=glb_category_var, all=TRUE)
row.names(glb_ctgry_df) <- glb_ctgry_df[, glb_category_var]
glb_ctgry_df <- merge(glb_ctgry_df,
myget_category_stats(obs_df=glb_newobs_df, mdl_id=glb_fin_mdl_id, label="new"),
by=glb_category_var, all=TRUE)
row.names(glb_ctgry_df) <- glb_ctgry_df[, glb_category_var]
if (any(grepl("OOB", glbMdlMetricsEval)))
print(orderBy(~-err.abs.OOB.mean, glb_ctgry_df[, -1])) else
print(orderBy(~-err.abs.fit.mean, glb_ctgry_df[, -1]))
print(colSums(glb_ctgry_df[, -grep(glb_category_var, names(glb_ctgry_df))]))
# print("Top category OOB errors:")
# print(glb_OOBobs_df[(glb_OOBobs_df[, glb_category_var] ==
# dsp_ctgry_df[1, glb_category_var]) &
# !glb_OOBobs_df[, predct_accurate_var_name],
# c(glb_id_var, glb_rsp_var_raw, paste0(glb_rsp_var_out, glb_sel_mdl_id),
# glb_category_var,
# row.names(head(myget_feats_importance(glb_sel_mdl), 5)),
# # "biddable", "startprice", "condition",
# glbFeatsText)])
}
if ((glb_rsp_var %in% names(glb_newobs_df)) &&
!(any(is.na(glb_newobs_df[, glb_rsp_var])))) {
print(sprintf("%s new confusion matrix & accuracy: ", glb_fin_mdl_id))
print(t(confusionMatrix(glb_newobs_df[, paste0(glb_rsp_var_out, glb_fin_mdl_id)],
glb_newobs_df[, glb_rsp_var])$table))
}
} ## [1] "Ensemble.repeatedcv.glmnet OOB confusion matrix & accuracy: "
## Prediction
## Reference N Y
## N 328 119
## Y 72 311
## .n.OOB .n.Fit .n.Tst .freqRatio.Fit .freqRatio.OOB
## iPadmini2 53 54 56 0.05294118 0.06385542
## iPad1 95 130 88 0.12745098 0.11445783
## iPad3 61 92 55 0.09019608 0.07349398
## iPadmini 123 154 111 0.15098039 0.14819277
## iPadmini3 36 54 38 0.05294118 0.04337349
## iPad4 73 84 68 0.08235294 0.08795181
## iPadAir2 69 102 62 0.10000000 0.08313253
## Unknown 96 108 92 0.10588235 0.11566265
## iPad2 142 144 154 0.14117647 0.17108434
## iPadAir 82 98 74 0.09607843 0.09879518
## .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean .n.fit
## iPadmini2 0.07017544 6.561706 0.12151308 54
## iPad1 0.11027569 8.833084 0.06794680 130
## iPad3 0.06892231 10.931711 0.11882295 92
## iPadmini 0.13909774 16.408094 0.10654606 154
## iPadmini3 0.04761905 7.819824 0.14481155 54
## iPad4 0.08521303 6.474186 0.07707364 84
## iPadAir2 0.07769424 12.850296 0.12598329 102
## Unknown 0.11528822 18.317750 0.16960880 108
## iPad2 0.19298246 11.492253 0.07980731 144
## iPadAir 0.09273183 6.386205 0.06516536 98
## err.abs.OOB.sum err.abs.OOB.mean err.abs.trn.sum
## iPadmini2 19.13157 0.3609729 20.25046
## iPad1 25.83490 0.2719463 35.67751
## iPad3 16.04787 0.2630799 28.88114
## iPadmini 31.95087 0.2597632 53.23321
## iPadmini3 9.28349 0.2578747 17.11551
## iPad4 18.11110 0.2480973 23.56793
## iPadAir2 16.83049 0.2439201 30.77085
## Unknown 22.85216 0.2380434 39.99713
## iPad2 31.11043 0.2190875 40.70949
## iPadAir 16.93299 0.2064999 23.99216
## err.abs.trn.mean .n.trn err.abs.new.sum err.abs.new.mean .n.new
## iPadmini2 0.1892567 107 NA NA 56
## iPad1 0.1585667 225 NA NA 88
## iPad3 0.1887656 153 NA NA 55
## iPadmini 0.1921777 277 NA NA 111
## iPadmini3 0.1901724 90 NA NA 38
## iPad4 0.1501142 157 NA NA 68
## iPadAir2 0.1799465 171 NA NA 62
## Unknown 0.1960644 204 NA NA 92
## iPad2 0.1423409 286 NA NA 154
## iPadAir 0.1332898 180 NA NA 74
## .n.OOB .n.Fit .n.Tst .freqRatio.Fit
## 830.000000 1020.000000 798.000000 1.000000
## .freqRatio.OOB .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean
## 1.000000 1.000000 106.075110 1.077279
## .n.fit err.abs.OOB.sum err.abs.OOB.mean err.abs.trn.sum
## 1020.000000 208.085884 2.569285 314.195397
## err.abs.trn.mean .n.trn err.abs.new.sum err.abs.new.mean
## 1.720695 1850.000000 NA NA
## .n.new
## 798.000000if (!is.null(glb_featsimp_df))
print(orderBy(as.formula(paste0("~ -", glb_sel_mdl_id, ".importance")),
subset(glb_featsimp_df, importance > 10)))## Ensemble.repeatedcv.glmnet.importance
## sold.fctr.predict.CSM.X.Train.gbm.prob NA
## sold.fctr.predict.CSM.X.Interact.Train.glmnet.prob NA
## sold.fctr.predict.CSM.X.Train.glm.prob NA
## sold.fctr.predict.CSM.X.Train.bagEarth.prob NA
## sold.fctr.predict.CSM.X.YeoJohnson.Train.bagEarth.prob NA
## sold.fctr.predict.CSM.X.center.scale.Train.bagEarth.prob NA
## sold.fctr.predict.CSM.X.spatialSign.Train.bagEarth.prob NA
## sold.fctr.predict.Max.cor.Y.Train.rpart.prob NA
## sold.fctr.predict.CSM.X.Train.avNNet.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.Train.rpart.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.1X1.Train.glmnet.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.3X1.Train.glmnet.prob NA
## sold.fctr.predict.CSM.X.Train.nnet.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.3X3.Train.glmnet.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.3X5.Train.glmnet.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.5X1.Train.glmnet.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.5X3.Train.glmnet.prob NA
## sold.fctr.predict.Max.cor.Y.rcv.5X5.Train.glmnet.prob NA
## sold.fctr.predict.RFE.X.Train.glm.prob NA
## sold.fctr.predict.CSM.X.Train.bayesglm.prob NA
## sold.fctr.predict.Interact.High.cor.Y.Train.glmnet.prob NA
## importance
## sold.fctr.predict.CSM.X.Train.gbm.prob 100.00000
## sold.fctr.predict.CSM.X.Interact.Train.glmnet.prob 90.27279
## sold.fctr.predict.CSM.X.Train.glm.prob 52.40344
## sold.fctr.predict.CSM.X.Train.bagEarth.prob 48.19262
## sold.fctr.predict.CSM.X.YeoJohnson.Train.bagEarth.prob 44.68916
## sold.fctr.predict.CSM.X.center.scale.Train.bagEarth.prob 44.48684
## sold.fctr.predict.CSM.X.spatialSign.Train.bagEarth.prob 44.22323
## sold.fctr.predict.Max.cor.Y.Train.rpart.prob 43.58650
## sold.fctr.predict.CSM.X.Train.avNNet.prob 42.05394
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.Train.rpart.prob 41.93871
## sold.fctr.predict.Max.cor.Y.rcv.1X1.Train.glmnet.prob 34.56317
## sold.fctr.predict.Max.cor.Y.rcv.3X1.Train.glmnet.prob 32.70668
## sold.fctr.predict.CSM.X.Train.nnet.prob 32.67550
## sold.fctr.predict.Max.cor.Y.rcv.3X3.Train.glmnet.prob 31.81654
## sold.fctr.predict.Max.cor.Y.rcv.3X5.Train.glmnet.prob 31.60895
## sold.fctr.predict.Max.cor.Y.rcv.5X1.Train.glmnet.prob 31.57097
## sold.fctr.predict.Max.cor.Y.rcv.5X3.Train.glmnet.prob 31.56534
## sold.fctr.predict.Max.cor.Y.rcv.5X5.Train.glmnet.prob 31.56464
## sold.fctr.predict.RFE.X.Train.glm.prob 31.56455
## sold.fctr.predict.CSM.X.Train.bayesglm.prob 16.99841
## sold.fctr.predict.Interact.High.cor.Y.Train.glmnet.prob 12.09660
## Final.Ensemble.glmnet.importance
## sold.fctr.predict.CSM.X.Train.gbm.prob 100.00000
## sold.fctr.predict.CSM.X.Interact.Train.glmnet.prob 90.27279
## sold.fctr.predict.CSM.X.Train.glm.prob 52.40344
## sold.fctr.predict.CSM.X.Train.bagEarth.prob 48.19262
## sold.fctr.predict.CSM.X.YeoJohnson.Train.bagEarth.prob 44.68916
## sold.fctr.predict.CSM.X.center.scale.Train.bagEarth.prob 44.48684
## sold.fctr.predict.CSM.X.spatialSign.Train.bagEarth.prob 44.22323
## sold.fctr.predict.Max.cor.Y.Train.rpart.prob 43.58650
## sold.fctr.predict.CSM.X.Train.avNNet.prob 42.05394
## sold.fctr.predict.Max.cor.Y.rcv.1X1.cp.0.Train.rpart.prob 41.93871
## sold.fctr.predict.Max.cor.Y.rcv.1X1.Train.glmnet.prob 34.56317
## sold.fctr.predict.Max.cor.Y.rcv.3X1.Train.glmnet.prob 32.70668
## sold.fctr.predict.CSM.X.Train.nnet.prob 32.67550
## sold.fctr.predict.Max.cor.Y.rcv.3X3.Train.glmnet.prob 31.81654
## sold.fctr.predict.Max.cor.Y.rcv.3X5.Train.glmnet.prob 31.60895
## sold.fctr.predict.Max.cor.Y.rcv.5X1.Train.glmnet.prob 31.57097
## sold.fctr.predict.Max.cor.Y.rcv.5X3.Train.glmnet.prob 31.56534
## sold.fctr.predict.Max.cor.Y.rcv.5X5.Train.glmnet.prob 31.56464
## sold.fctr.predict.RFE.X.Train.glm.prob 31.56455
## sold.fctr.predict.CSM.X.Train.bayesglm.prob 16.99841
## sold.fctr.predict.Interact.High.cor.Y.Train.glmnet.prob 12.09660print("glb_newobs_df prediction stats:")## [1] "glb_newobs_df prediction stats:"if (glb_is_regression)
print(myplot_histogram(glb_newobs_df, paste0(glb_rsp_var_out, glb_fin_mdl_id)))
if (glb_is_classification)
print(table(glb_newobs_df[, paste0(glb_rsp_var_out, glb_fin_mdl_id)]))##
## N Y
## 376 422# Use this to see how prediction changes by changing one or more values
# players_df <- data.frame(id=c("Chavez", "Giambi", "Menechino", "Myers", "Pena"),
# OBP=c(0.338, 0.391, 0.369, 0.313, 0.361),
# SLG=c(0.540, 0.450, 0.374, 0.447, 0.500),
# cost=c(1400000, 1065000, 295000, 800000, 300000))
# players_df$RS.predict <- predict(glb_models_lst[[csm_mdl_id]], players_df)
# print(orderBy(~ -RS.predict, players_df))
# dsp_chisq.test(Headline.contains="[Vi]deo")
if ((length(diff <- setdiff(names(glb_trnobs_df), names(glb_allobs_df))) > 0) ||
(length(diff <- setdiff(names(glb_fitobs_df), names(glb_allobs_df))) > 0) ||
(length(diff <- setdiff(names(glb_OOBobs_df), names(glb_allobs_df))) > 0) ||
(length(diff <- setdiff(names(glb_newobs_df), names(glb_allobs_df))) > 0)) {
print(diff)
stop("glb_*obs_df not in sync")
}
if (glb_save_envir)
save(glb_feats_df, glb_allobs_df,
#glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file = paste0(glb_out_pfx, "prdnew_dsk.RData"))
sav_fin_mdl <- glb_fin_mdl; sav_sel_mdl <- glb_sel_mdl
#save(sav_fin_mdl, sav_sel_mdl, file=paste0(glb_out_pfx, "sav_mdl.RData"))
# load(file=paste0(glb_out_pfx, "sav_mdl_01.RData"), verbose=TRUE)
# prv_fin_mdl <- sav_fin_mdl; prv_sel_mdl <- sav_sel_mdl
# load(file=paste0(glb_out_pfx, "sav_mdl.RData"), verbose=TRUE)
# cur_fin_mdl <- sav_fin_mdl; cur_sel_mdl <- sav_sel_mdl
# all.equal(cur_fin_mdl, prv_fin_mdl)
# cur_fitobs_df <- cur_fin_mdl$trainingData; prv_fitobs_df <- prv_fin_mdl$trainingData; all.equal(cur_fitobs_df, prv_fitobs_df)
# nrow(cur_fitobs_df); nrow(prv_fitobs_df)
# names(cur_fitobs_df); names(prv_fitobs_df)
# all.equal(cur_fin_mdl$bestTune, prv_fin_mdl$bestTune)
# all.equal(glb_sel_mdl, sav_sel_mdl)
# cur_fitobs_df <- cur_sel_mdl$trainingData; prv_fitobs_df <- prv_sel_mdl$trainingData; all.equal(cur_fitobs_df, prv_fitobs_df)
# head(myget_feats_importance(glb_sel_mdl)); head(myget_feats_importance(sav_sel_mdl))
# head(myget_feats_importance(cur_sel_mdl)); head(myget_feats_importance(prv_sel_mdl))
# tmp_replay_lst <- replay.petrisim(pn=glb_analytics_pn,
# replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
# "data.new.prediction")), flip_coord=TRUE)
# print(ggplot.petrinet(tmp_replay_lst[["pn"]]) + coord_flip())
glb_chunks_df <- myadd_chunk(glb_chunks_df, "display.session.info", major.inc=TRUE)## label step_major step_minor label_minor bgn
## 16 predict.data.new 8 0 0 2943.586
## 17 display.session.info 9 0 0 2989.018
## end elapsed
## 16 2989.018 45.432
## 17 NA NANull Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.
## label step_major step_minor label_minor bgn
## 14 fit.data.training 7 0 0 1519.888
## 11 fit.models 6 1 1 377.259
## 5 extract.features 3 0 0 30.349
## 12 fit.models 6 2 2 1302.250
## 15 fit.data.training 7 1 1 2875.216
## 10 fit.models 6 0 0 311.876
## 7 cluster.data 3 2 2 250.941
## 16 predict.data.new 8 0 0 2943.586
## 13 fit.models 6 3 3 1488.119
## 1 import.data 1 0 0 10.959
## 9 select.features 5 0 0 300.026
## 2 inspect.data 2 0 0 24.130
## 3 scrub.data 2 1 1 28.730
## 8 partition.data.training 4 0 0 299.110
## 4 transform.data 2 2 2 29.758
## 6 manage.missing.data 3 1 1 250.784
## end elapsed duration
## 14 2875.215 1355.328 1355.327
## 11 1302.249 924.990 924.990
## 5 250.783 220.434 220.434
## 12 1488.118 185.868 185.868
## 15 2943.585 68.369 68.369
## 10 377.259 65.383 65.383
## 7 299.109 48.169 48.168
## 16 2989.018 45.432 45.432
## 13 1519.887 31.769 31.768
## 1 24.129 13.171 13.170
## 9 311.875 11.849 11.849
## 2 28.730 4.600 4.600
## 3 29.758 1.028 1.028
## 8 300.025 0.915 0.915
## 4 30.348 0.591 0.590
## 6 250.940 0.157 0.156
## [1] "Total Elapsed Time: 2,989.018 secs"